Wearable apparatus to detect sleep stage information of an individual

ABSTRACT

A monitor device and associated methodology are disclosed which provide a self contained, relatively small and continuously wearable package for the monitoring of heart related parameters, including ECG. The detection of heart related parameters is predicated on the location of inequipotential signals located within regions of the human body conventionally defined as equivalent for the purpose of detection of heart related electrical activity, such as on single limbs. Amplification, filtering and processing methods and apparatus are described in conjunction with analytical tools for beat detection and display.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of co-pending U.S. application Ser.No. 12/840,109, filed Jul. 20, 2010 entitled A Wearable Apparatus ForMeasuring Heart-Related Parameters And Deriving Human Status ParametersFrom Sensed Physiological And Contextual Parameters, which isincorporated herein by reference in its entirety. U.S. application Ser.No. 12/840,109 is a continuation of co-pending U.S. application Ser. No.11/928,302 entitled Method And Apparatus For Measuring Heart-RelatedParameters And Deriving Human Status Parameters From SensedPhysiological And Contextual Parameters, filed Oct. 30, 2007, which isincorporated herein by reference in its entirety. U.S. application Ser.No. 11/928,302 is a continuation of co-pending U.S. application Ser. No.10/940,889 entitled Method and Apparatus for Measuring Heart RelatedParameters filed Sep. 13, 2004, which claims the benefit of U.S.Provisional Application Ser. No. 60/502,764, Sep. 12, 2003; U.S.Provisional Application Ser. No. 60/510,013, filed Oct. 9, 2003; andU.S. Provisional Application Ser. No. 60/555,280, filed Mar. 22, 2004,each of which are incorporated herein by reference in their entirety.

BACKGROUND

1. Field

The present invention relates to a method and apparatus for accuratelymeasuring heart related parameters from within a conventionally definedequivalence region of the human body. More particularly, a method andapparatus is disclosed for measuring an ECG signal and other heartrelated parameters such as heart beats or heart rate from a single limbof the human body. Most specifically, the heart related parameters aretaken from the upper left or right arm.

2. Description of the Related Art

The heart is a muscular pump that is controlled by a natural electricalsystem that causes the heart muscle to contract and pump blood throughthe heart to the lungs and the rest of the body, carrying oxygen as wellas other needed nutrients. The heart can be characterized by a set ofparameters that describe the state of the heart, including the frequencyand timing of the contractions of the four chambers of the heart, andthe pattern of electrical signals causing those contractions. There aremany methods of detecting these parameters that are well known in theart, including: sensing the electrical impulses of the heart, sensingthe pulse of blood as it moves through arteries, Doppler and otheracoustic based methods, capacitance, micro-impulse radar, pressure-and/or motion-based methods such as by utilizing piezo-electric elementsor strain gauges, and optical methods in areas where the pulsing ofblood can be externally viewed, such as in a pulse-oximeter.

The most well-known and conventional method utilized today for measuringheart-related parameters is the electrocardiogram. An electrocardiogram,or ECG, signal is a surface measurement of the electrical potential ofthe heart generated by electrical activity in cardiac tissue. Thismeasurement can be made using electrodes placed on the surface of theskin because the entire body is capable of conducting electricity.

FIG. 1 shows a typical ECG signal generated by one heart beat. Signalstrength is shown on the Y axis and time is shown on the X axis. Theindividual spikes and dips in the signal are called waves. The P waveshown in FIG. 1 represents the contraction of the atria. The Q, R, and Swaves, referred to as the QRS complex, represent the contraction of theventricles. The T wave represents the recovery, or repolarization, ofthe ventricles. The amplitude of a typical ECG signal is approximately 1to 2 mV when measured from the chest using good electrode contacts.

ECG measurements may be used to provide information about a number ofheart related parameters, including, but not limited to, the heart beatrate, or heart rate, for a number of applications, such as medicaldiagnostic, health awareness and sports performance applications. Themost reliable heart rate calculation based upon ECG is performed bydetecting each QRS complex, and thus each heart beat, because the QRScomplex contains the highest amount of energy and its spectrum differssufficiently from the spectrum of movement artifacts. Beats aretypically counted at each R point (the peak), and the distance between afirst R point and a subsequent R point is known as the R-R interval,which, when inverted, yields the instantaneous heart rate. Otherparameters such as the heart-rate variability are also computable fromthe set of R-R intervals.

As discussed above, the heart is a source of a voltage potentialdifference resulting from the electrical activity that causes the heartmuscles to contract. This potential difference is known in the art asthe heart's action potential. An ECG signal is a measurement of thisaction potential. In addition, the heart is positioned in the left chestarea and is oriented at an angle slightly off of vertical. Thetraditional model of ECG measurement indicates that ECG measurementsmust be taken across the heart, meaning using electrodes placed oneither side of an imaginary line running through the center of theheart. Many different researchers have identified the various sectionsof the surface of the body in different ways with respect to placingelectrodes for measuring different aspects of the heart's electricalactivity.

Generally, these placements are identified in two ways. First, pairs ofelectrodes are often used to measure the electrical potential differencebetween two points. If two points show an electrical potential signalthat varies with the activity of the heart they are said to be notequipotential or therefore inequipotential with respect to one another.Inequipotential therefore refers solely to the difference in the heart'saction potential rather than other sources of voltage difference such asEMG. Furthermore, locations are described herein as measuring adifferent aspect of the heart's electrical activity from other locationswhen those two locations are inequipotential. The electrodes areconventionally placed in a manner as to obtain maximum differentiationbetween the electrodes. Conventionally, therefore, the body is dividedinto quadrants I, II, III and IV, as illustrated in FIG. 1A. Electrodesare conventionally placed in two different quadrants on the body, wherethe body 1 is divided into four sections, or quadrants, by two planesrunning through the heart. The location of these planes has beenmodified over time as knowledge in this field has progressed, but hasremained fairly constant in that sagittal plane 2 runs roughlyvertically through the heart and the transverse plane 3 roughlyhorizontally. These two planes are orthogonal to one another when viewedfrom the two-dimensional perspective from in front of the patient. It isimportant, for the purposes of this application, to assess the locationof these imaginary planes through the heart. Sagittal plane 2, issometimes considered to be coincident with the medial line of the body.Other views, however, direct the vector along a more canted axiscoincident with the slightly asymmetric orientation of the heart withinthe chest cavity. Transverse plane 3, is orthogonal to sagittal plane 2.For bipolar electrode placements, the two electrodes are conventionallyplaced in two different quadrants, allowing the measurement of theheart's action potential. The other method of reading ECG signals fromthe heart is to take single pole readings that utilize a singleelectrode at one point and then utilize an average of multipleelectrodes for the other point. This allows a view of the heart fromdifferent directions, and allows the creation of views of the heart notachievable with only two electrodes. The precordial, or chest placementsin the standard 12-lead ECG are examples of this sort of placement.

Other models include the Einthoven triangle, which describes a roughlyinverted equilateral triangular region on the chest having a baseextending between the left and right shoulder joints and an apexapproximately located at the base of the ribcage, below the sternum. Themodel contemplates the angle formed at the right shoulder having a firstaspect of the ECG signal, the abdominal angle having a second suchaspect and the left shoulder angle having a third aspect. The Bayleytriaxial system and the Hexaxial system each divide the chest andabdominal area into a larger number of sections or regions, each ofwhich is assigned a single aspect or mixed aspect of the ECG signal.

All of the prior art location identification systems require electrodesplaced in at least two of the quadrants of the body. The surface area ofeach quadrant is defined herein, therefore, as an equivalence region onthe body, the portions of the body near the boundaries of the quadrantsare further eliminated from such equivalence regions, as it is commonlyunderstood that the boundary can move slightly as the heart beats, theperson moves, and that the boundaries can be different between differentindividuals due to minor difference in heart orientation within thebody. The equivalence regions are thus defined as the quadrantsillustrated in FIG. 1A. Previous systems for measuring ECG all requirehaving electrodes in at least two of the equivalence regions. Theseequivalence regions as well as the plethora of different mappingsapplied to the surface of the body utilized in the prior art can also beunderstood as following the principle that the signals obtainable withinthese quadrants are homogeneous as it is assumed that the body iscomposed of a homogeneous material.

Several prior art devices exist for measuring ECG based on thetraditional model. For example, clinical or medical ECG devices useseveral electrodes placed on the chest, arms and legs to measure anumber of different ECG signals from selected electrode pairs wherein ineach pair, one electrode is located in one equivalence region and theother electrode is located in a different equivalence region. Thedifferent readings together allow a clinician to get a view of thefunction of the three dimensional electrical activity of the heart froma number of different angles. In many cases, the devices which providethe ability to detect and monitor the heart related parameters isstationary and is intended to monitor a stationary patient.

Such devices, while highly accurate, are very expensive and cumbersomeand thus do not lend themselves well to ambulatory or long term usessuch as in a free living environment. Holter monitors are devices thatmay be used for continuous, ambulatory ECG measurement, typically over a24-48 hour time period. These Holter devices collect raw electrical dataaccording to a preset schedule, or frequency, typically 128 hz or 256hz. These devices must therefore contain a significant amount of memoryand/or recording media in order to collect this data. The physical bulkand inconvenient accessories of this device restricts its continuous useto a relatively short time frame. Each device comprises at least twoelectrodes for clinical or monitoring data detection and typically athird electrode for ground. The leads are designed to be attached to thechest across the heart, or at least across the conventionally understoodsagittal plane 2, and a monitoring device connected to the electrodes iscarried or worn by the patient, which is typically a heavy rectangularbox clipped to the patient's waist or placed in a shoulder bag. Thesensors utilized in conjunction with the device are affixed according toa clinical procedure, wherein the skin under the electrode or sensor isshaved and/or sanded and cleaned with skin preparation liquids such asalcohol prior to application to improve signal quality. Consequently,the sensors are not easily interchanged and may limit physical orhygienic activity. Holter monitors are relatively expensive and for thereasons listed above, are not comfortable for long term and/or activewear situations.

Loop monitors are configured and worn similarly, yet are designed towork for longer periods of time. These systems are designed to recordshorter segments or loops of raw data or morphology when the wearersignifies, by pressing a time stamp button that they are doing anactivity of interest or feeling a chest or heart related pain. Thedevice will typically record 30 seconds before and 30 seconds after thetime stamp. While some success with respect to longer term wearabilityand comfort is achieved, these loop monitor devices are stillinconvenient for everyday use, and include lead wires from the device,snap on sensors affixed to the body by adhesives which require dailyskin preparation and periodic re-alignment of the sensors to theoriginal positions.

More recently, a few monitors have also been provided with someautomated features to allow the device, without human intervention, torecord certain loops when certain preset conditions or measurementthresholds are achieved by the detected heart related activity, such asan abnormal beat to beat interval or a spike in heart rate. Implantableloop recorders have also been developed, which provide similarfunctionality, with the attendant inconvenience and risks associatedwith an invasive implant.

Another diagnostic device is known as an event recorder, and this deviceis a hand held product, with two electrodes on the back, some desireddistance apart with recording capabilities where a patient is instructedto place this device against the skin, over the heart or across thesides of the body in order to record a segment of data when the patientis feeling a heart related symptom. This device is not utilized forcontinuous monitoring, and has memory capability for only a limitednumber of event records. Once the media storage is filled, there is afacility on the device to communicate the data back to a clinic,clinician, service, or doctor for their analysis, usually by telephone.

While not designed for medial or clinical applications per se, a numberof chest strap heart rate monitors have been developed that may be usedto measure heart rate from ECG, with some recent devices being capableof recording each detected heart beat, recorded in conjunction with atime stamp in the data. Examples of such conventional monitorscommercially available include Polar Electro Oy, located in Oulu,Finland and Acumen, Inc. located in Sterling, Va. These chest strapmonitors are designed to be wrapped around the torso beneath the chestand include two electrodes positioned on either side of the heart'sconventionally understood transverse plane 3 for measuring an ECGsignal. The device is placed just below the pectorals, with conventionalelectrode positioning. The device is placed at this location becausenoise and motion signal artifacts from muscle activity is minimal andthe signal amplitude is quite robust, consistent and discernable by acircuit or software application. Chest strap monitors of this type,while promoted for use in exercise situations, are not particularlycomfortable to wear and are prone to lift off of the body during use,particularly when the wearer lies on his or her back.

Finally, a number of watch-type ECG based heart rate monitors arecommercially available, such as the MIO watch sold by Physi-CalEnterprises LP, located in Vancouver, British Columbia. Such watchesinclude a first electrode attached to the back of the watch that, whenworn, contacts one arm of the wearer, and one or more second electrodesprovided on the front surface of the watch. To get an ECG signal, andthus a heart rate, a wearer must touch the second electrode(s) with afinger or fingers on the opposite hand, that is, the hand of the arm notwearing the watch. Thus, despite being worn on one arm, the watchmeasures ECG according to the conventional method, being across theheart, again on either side of the heart's conventionally understoodsagittal plane 2, because the two electrodes are contacting both arms.Such watches, while comfortable to wear, only make measurements whentouched in this particular manner and thus are not suitable formonitoring ECG and heart rate continuously over long periods of time orwhile conducting everyday activities such as eating, sleeping,exercising or even keyboarding at a computer.

Matsumara, U.S. Pat. No. 5,050,612, issued Sep. 24, 1991, discloses theuse of a multi-electrode sensing watch device, identified as theHeartWatch, manufactured by Computer Instruments Corporation, Hampstead,N.Y., for certain types of heart parameter detection. While Matsumaradiscloses that the conventional use of the HeartWatch device is inconjunction with a chest strap, he also identifies an alternative usewhich relies solely on the multisensor watch device itself. The devicehas two electrodes at different distances along the arm from the heart,and the detected waveform from one electrode is subtracted from theother to obtain a resultant signal. Matsumura identifies this signal asnot resembling an ECG, but states that it is useful for detecting STsegment depression. No teaching or suggestion of the efficacy of thismethod for the identification of heart rate or other heart relatedparameters is made.

As described above, the traditional models of ECG measurement do notcontemplate the action potential of the heart, and thus ECG, beingdetected and measured from two points within a single quadrant or withina single equivalence region. Moreover, the traditional model rejects themeasurement of the action potential from two locations on the same limb.The prior art does contemplate some sensor placements which takeadvantage of the three dimensional nature of the human body and allowfor measuring the heart's action potential between electrodes placed onthe front and back of the body, or between spots high on the torso andlow on the torso, but on the same side of the body. One skilled in theart would recognize that the prior art only utilized sensor placementsthat included two or more electrodes in multiple quadrants orequivalence regions.

Another significant shortcoming of ambulatory devices is electricalnoise. Noise is detected from both ambient sources surrounding the body,movement and organ noise within the body, and most significantly, themovement of the body itself, including muscle artifacts, motionartifacts, skin stretching and motion between the electrode and theskin. A variety of patents and other references relate to the filteringof noise in many systems, including heart rate detection. In Zahorian,et al., U.S. Pat. No. 5,524,631, issued Jun. 11, 1996, a system isdisclosed for detecting fetal heart rates. A significant noise problemexists in that environment, including the heart action of the mother, aswell as the significant noise and distortion caused by the fetus'location within a liquid sac inside the mother's abdomen. Zahorianutilizes multiple parallel non linear filtering to eliminate such noiseand distortion in order to reveal the fetus' heart rate. The system,like many of the prior art, is unconcerned with the wearability of themonitoring device or the ability to continuously monitor the subjectover a long period of time.

None of the above systems identified above combine wearability andaccuracy in a compact device. What is lacking in the art, therefore, isa device which provides the ability to measure ECG from two locations ina single equivalence region, such as within a single quadrant as shownin FIG. 1A or on a single limb. Although there are some examples in theprior art that recognize the possibility of inequipotential pairs withina single equivalence region, that the teachings of the prior art fail toutilize these pairs for obtaining a viable signal. There are severalbarriers to the ability to utilize these signals from unconventionallocations, including the small amplitude of the signal, which can beless than one tenth of the signal measured at most conventionally placedelectrode locations, the high amount of noise with respect to thatsignal, as well as the significant effort and risk required to overcomelimitations in accuracy, amplitude, and noise obtained fromunconventional placements. What is further lacking in the art is such adevice which is relatively small in size and adapted for longer periodsof continuous wear and monitoring, in conjunction with sensors whichminimize the requirement of clinical observation, application orpreparation. Such a device provides new opportunities for continuousheart monitoring, including improved comfort, less complex products, andimproved compliance with monitoring. Additionally, what is lacking inthe art is the ability to combine the continuous monitoring of the heartrelated parameters with a device which can detect, identify and recordthe physical activities of the wearer and correlate the same to theheart related parameters.

SUMMARY

A monitor device and associated methodology are disclosed which providea self contained, relatively small and wearable package for themonitoring of heart related parameters, including ECG. The monitordevice is primarily a simple, unobtrusive housing which is wearable inthe sense that it is temporarily affixed to the user's body, but alsowearable in the sense described in Stivoric, et al., U.S. Pat. No.6,527,711, issued Mar. 4, 2003, the disclosure of which is incorporatedby reference hereto. Stivoric teaches that the sizing, flexibility andlocation of items attached to the body significantly affect the abilityof the wearer to recognize the item as part of the body, reducing theirritation factor associated with wearing such an item for extendedperiods of time. Furthermore, the use of the appropriate shapes,materials and locations reduces the interference of the item with normalbody movement and activity. Each of these factors increases thewearability of the item and therefore increases the compliance of thewearer with the need for long term and continuous wear.

More specifically, the monitor device may be of a type described inTeller, et al., U.S. Pat. No. 6,605,038, issued Aug. 12, 2003, thespecification of which is incorporated herein by reference. The primaryfocus of the monitor device itself is to provide the functionalitydescribed below in a housing or other package which is comfortable forlong term wear, remains in place during normal daily activity so as tocontinuously provide a quality signal or data record and also reducesthe noise or other interference to that signal or record created by thedevice itself. One focus of the device is to provide a self-containedhousing which incorporates all or at least the majority of the operatinghardware. The monitor device, in addition to the Teller device, mayfurther include, as an accessory or rigid housing substitute, a largesized adhesive strip, similar to that used for cuts and abrasions whichcontains the sensor package within the current location of the absorbentmaterial. Reduction of weight and bulk is very important to increasingthe ability for the device to remain affixed in both the right locationand with proper contact to the body, especially under rigorousconditions, such as exercise. The device is easy to put on and take offwithout need for extensive or clinical skin preparation, if any. Thedevice is provided with an appropriate type and strength of adhesiverequired to keep the weight of the device from disconnecting any snapsor other connections, or pulling the electrode off of the skin. Oneprimary advantage of the device is the elimination of long lead wireswhich, in addition to being unsightly and inconvenient, act as largeantennas for creating noise input to the system. Reduction in the amountof snap connections also reduces these noises, which are common forHolter and loop devices. While not necessarily possible with the currentstate of processor and sensor size, it is clearly contemplated that theinstant system, given the appropriate miniaturization of hardware, couldbe as simple as sliding on a watch or pair of glasses, utilizing thesame basic methodology and equipment identified herein.

Specifically, a monitoring device is disclosed which includes at leastone or more types or categories of sensors adapted to be worn on anindividual's body. The sensor or sensors, which may include multipleelectrodes or other subordinate sensing devices of equivalent type, maybe drawn from the categories of contextual and physiological sensors.The physiological sensors may be selected from the group consisting of:respiration sensors, temperature sensors, heat flux sensors, bodyconductance sensors, body resistance sensors, body potential sensors,brain activity sensors, blood pressure sensors, body impedance sensors,body motion sensors, oxygen consumption sensors, body chemistry sensors,blood chemistry sensors, interstitial fluid sensors, body positionsensors, body pressure sensors, light absorption sensors, body soundsensors, piezoelectric sensors, electrochemical sensors, strain gauges,and optical sensors. Sensors are incorporated to generate dataindicative of detected parameters of the individual. There may be one ormore such parameters of the individual, with at least one such parameterbeing a physiological parameter. The apparatus also includes a processorthat receives at least a portion of the data indicative of at least onephysiological parameter. Preferably, the device is specifically directedto the detection of a single heart related parameter, heart beats. It isto be specifically understood that additional parameters may be detectedwith or without additional sensors. The processor may be adapted togenerate derived data from at least a portion of the data indicative ofsuch detected parameters, wherein the derived data comprises anadditional parameter of the individual. The additional parameter is anindividual status parameter that cannot be directly detected by any ofthe sensors.

The sensors may be physiological sensors, or may be at least onephysiological sensor and one or more optional contextual sensors. Themonitoring device may further include a housing adapted to be worn onthe individual's body, wherein the housing supports the sensors orwherein at least one of the sensors is separately located from thehousing. The apparatus may further include a flexible body supportingthe housing having first and second members that are adapted to wraparound a portion of the individual's body. The flexible body may supportone or more of the sensors. The apparatus may further include wrappingmeans coupled to the housing for maintaining contact between the housingand the individual's body, and the wrapping means may support one ormore of the sensors.

The monitoring device may include, or, optionally, be utilized inconjunction with an external a central monitoring unit remote from theat least two sensors that includes a data storage device. The datastorage device receives the derived data from the processor andretrievably stores the derived data therein. The apparatus also includesmeans for transmitting information based on the derived data from thecentral monitoring unit to a recipient, which recipient may include theindividual or a third party authorized by the individual. The processormay be supported by a housing adapted to be worn on the individual'sbody, or alternatively may be part of the central monitoring unit.

As a further alternative embodiment, rather than the processor providedin the monitoring device being programmed and/or otherwise adapted togenerate the derived or other calculated data, a separate computingdevice, such as a personal computer, could be so programmed. In thisembodiment, the monitoring device collects and/or generates the dataindicative of various physiological and/or contextual parameters of theuser, which is stored in the memory provided. This data is thenperiodically uploaded to a computing device which in turn generatesderived data and/or other calculated data. Alternatively, the processorof the monitoring device could be programmed to generate the deriveddata with the separate computer being programmed and/or otherwiseadapted to include the utilities and algorithms necessary to createfurther or secondary derivations based on the physiological and/orcontextual data, the first level data derived therefrom, data manuallyinput by the user and/or data input as a result of device-to-deviceinteraction uploaded from the monitoring device or a cooperative thirddevice. The computing device in these alternative embodiments may beconnected to an electronic network, such as the Internet, to enable itto communicate with a central monitoring unit or the like.

The apparatus may be further adapted to obtain or detect life activitiesdata of the individual, wherein the information transmitted from thecentral monitoring unit is also based on the life activities data. Thecentral monitoring unit may also be adapted to generate and providefeedback relating to the degree to which the individual has followed asuggested routine. The feedback may be generated from at least a portionof at least one of the data indicative of a physiological parameter, thederived data and the life activities data.

The central monitoring unit may also be adapted to generate and providefeedback to a recipient relating to management of an aspect of at leastone of the individual's health and lifestyle. This feedback may begenerated from at least one of the data indicative of a first parameter,the data indicative of a second parameter and the derived data. Thefeedback may include suggestions for modifying the individual'sbehavior.

The system is designed to collect data continuously, with no interactionof the wearer necessary, but such interaction is permitted foradditional functionality such as particular time stamping capabilities,as necessary. The ability to continuously monitor the heart relatedparameters limits the need for a manual trigger at the time of an eventor the detection of a threshold condition based upon the status of thederived data, as described above. While the system is designed tocollect data continuously, in some embodiments the user may utilize thetimestamp button to signal that certain heart rate parameters should becollected for the time period around the timestamp. An additionalfunctionality of the device is context and activity detection. Throughthe use of both the physiological and contextual sensors provided in thedevice, the ability to learn, model, or ascertain what combinations ofdata parameters relate to certain activities can be achieved. Theability to detect and discern the type of activity in which the user isengaged relieves the user of the need to manually log these activitiesto correlate with the heart output data during subsequent review.

The functionality of the monitoring devices is predicated upon thedetection of multiple inequipotential heart parameter signals within asingle equivalence region of the body and more particularly, multipledetectable action potential signals on a single limb. The device andmethods identify and monitor certain pairs of points on the body toobtain inequipotential signals with respect to the heart's actionpotential. The location of the sensors is therefore determined by theirrelationship to these detectable inequipotential action potentialsignals, which may be arranged about the planes illustrated in FIG. 1Awhich separate the various quadrants of the human body.

It is specifically contemplated that the physical form and/or housingfor the device is not limited to those embodiments illustrated herein.Additional embodiments which require more flexibility, or are intendedto be disposable in nature may eliminate the housing entirely andinclude the electronic and other functionality in a more temporary orflexible container, such as a patch, which may have tentacle-likeextensions or separately wired sensors attached thereto. Preferredlocations for the device itself include, the deltoid and tricep upperarm locations identified specifically herein, the back of the base ofthe neck and adjacent medial shoulder area, the sides of the chestadjacent to the upper arms when at rest along the sides of the body andthe femoral areas of the left and right lower front abdomen adjacent thepelvis.

Additionally, the device may be combined with other like devices in acooperative array, which may be utilized to further process or analyzethe signals derived therefrom. For example, in the case of a pregnantwoman, a first such device may be positioned to detect the mother'sheart related parameters in a location unlikely to detect the fetalheart related parameters and a second such device, especially in theform of an adhesive or other patch, might be located immediatelyadjacent the fetus on the mother's abdomen. The signals from themother's device could be utilized to eliminate the noise of the mother'sheart related parameters from the fetus' data stream.

Feedback from the system can take many forms, including the standardvisual graphical methods, but a preferred embodiment would include audiofeedback as well. This audio component may be in the form of a soundthat resonates/conducts through the body, like a bone phone or othervariant, to make this feeling more intimate and body like, even if thesound is manufactured digitally to represent the beat. A digital oranalog stethoscope could be included in the system to assist in theproduction of an appropriate sound. Such a device on the abdomen couldalternatively be made up of an array of Doppler or ECG electrodes toreduce the need to search for the most appropriate signal location. Thedevice may also be adapted to work in conjunction with an implantabledevice or other consumed data detector.

Further features and advantages of the present invention will beapparent upon consideration of the following detailed description of thepresent invention, taken in conjunction with the following drawings.

BRIEF DESCRIPTION OF THE FIGURES

The invention and the following detailed description of certainembodiments thereof may be understood by reference to the followingfigures:

FIG. 1 is a representation of a typical ECG signal;

FIG. 1A is a diagrammatic representation of the upper section of thehuman body showing equivalence quadrants;

FIGS. 2A, 2B and 2C are back, front and back views, respectively, of theleft arm showing electrode placement locations according to an aspect ofthe present invention;

FIGS. 3A and 3B are back and front views, respectively, of the right armshowing electrode placement locations according to an aspect of thepresent invention;

FIGS. 3C, 3D and 3E are front, back and front views, respectively of thetorso showing electrode placement locations according to an aspect ofthe present invention;

FIG. 4 is a block diagram of a circuit for detecting an ECG signal fromaccording to an embodiment of the present invention;

FIGS. 5A and 5B are circuit diagrams of first and second embodiments ofthe bias/coupling network shown in FIGS. 4 and 7;

FIG. 5C is a circuit diagram of a first stage amplifier design;

FIG. 6 is a circuit diagram of one embodiment of the filter shown inFIGS. 4 and 7;

FIG. 7 is a block diagram of a circuit for detecting an ECG signalaccording to an alternate embodiment of the present invention;

FIGS. 7A through 7D are diagrammatic representations of detected ECGsignals through various stages of processing;

FIGS. 7E through 7H are diagrammatic representations of detected ECGsignals through various states of beat detection;

FIGS. 8A through 8F are block diagrams of alternative circuits fordetecting an ECG signal according to an alternate embodiment of thepresent invention;

FIG. 9 is a diagram of a typical peak forming part of the signalgenerated according to the present invention;

FIG. 10 is a diagram of a typical up-down-up sequence forming a part ofthe signal generated according to the present invention; FIGS. 10A-10Bare illustrations of heart rate data showing overlapping time slices;

FIG. 11 is a block diagram showing the format of algorithms that aredeveloped according to an aspect of the present invention;

FIG. 12 is a block diagram illustrating an example algorithm forpredicting energy expenditure according to an aspect of the presentinvention; FIG. 12A is an illustration of heart rate data presented withother physiological data and annotations;

FIG. 13 is an isometric view of an armband body monitoring device;

FIG. 14 is a bottom plan view of one embodiment of the armbandmonitoring device;

FIG. 15 is a bottom plan view of a second embodiment of the armbandmonitoring device;

FIG. 16 is a bottom plan view of a third embodiment of the armband bodymonitoring device;

FIG. 17 is a bottom plan view of a fourth embodiment of the armband bodymonitoring device;

FIG. 18 is a bottom plan view of a fifth embodiment of the armband bodymonitoring device;

FIG. 19 is a bottom plan view of a sixth embodiment of the armband bodymonitoring device;

FIG. 20 is a bottom plan view of a seventh embodiment of the armbandmonitoring device;

FIG. 21 is an isometric view of the seventh embodiment of the armbandbody monitoring device mounted upon a human arm;

FIG. 22 is an isometric view of an eighth embodiment of the armband bodymonitoring device.

FIG. 23A is a top plan view of a ninth embodiment of the armband bodymonitoring device.

FIG. 23B is a bottom plan view of a ninth embodiment of the armband bodymonitoring device.

FIG. 23C is a sectional view of the embodiment of FIG. 23B taken alongline A-A.

DETAILED DESCRIPTION

Conventional thinking in the field of cardiology/ECG is that an ECGsignal must be measured across the heart, meaning with electrodes placedin two different quadrants of the heart's conventionally definedsagittal and transverse planes. A device and methodology are disclosedherein which permits the measurement of an ECG signal from certain pairsof points located within regions or areas of the human body previouslyconsidered inappropriate for such measurement. The device andmethodology disclosed herein focus on the identification of certainlocations on the body within the previously defined equivalence regionsutilized for electrode location. Many of these electrode locations arewithin a single quadrant, i.e., when the electrode locations areconnected geometrically directly through tissue, the line describedthereby does not cross into another quadrant.

In other words, certain points within one quadrant are correlated withthe electropotential of the ECG signal conventionally associated with adifferent quadrant because the potential from the opposite side has beentransported to that point internally through what appear to be lowimpedance non-homogeneous electropotential or electrical pathwaysthrough the body, which may be analogized as internal signal leadswithin the tissue. This methodology therefore focuses on two differentaspects of the ECG signal, rather than more narrowly defining theseaspects as emanating from certain quadrants of the body. Thus, contraryto the teachings of the prior art, an ECG signal may be detected andmeasured using pairs of electrodes placed within a single quadrant, butdetecting a significant electrical potential difference between the twopoints. In other words, the two points are inequipotential with respectto one another. In most instances, it is more helpful to envision theelectrode locations being located within independent regions of skinsurface, separated by a boundary which may be planar or irregular.

In the preferred embodiment of the present invention, pairs of locationson or near the left arm have been identified for placement of electrodesto detect the different aspects of the ECG signal. It is to be notedthat similar sites within equivalence regions are found at a myriad oflocations on the human body, including the right and left arms, theaxillary area under the arms, the anterior femoral area adjacent thepelvis, the back of the base of the neck and the base of the spine. Morespecifically, certain locations on the left arm carry an aspect of theECG signal and certain locations on or near the left arm carry adifferent aspect of the ECG signal. It is also to be specifically notedthat anatomical names, especially names of muscles or muscle groups, areused to identify or reference locations on the body, though placement ofthe electrodes need only be applied to the skin surface directlyadjacent these locational references and are not intended to beinvasive. Referring now to FIGS. 2A and 2B, which are drawings of theback and front of the left arm, respectively, the inventors have foundthat the left wrist 5, left triceps muscle 10, and the left brachialismuscle 15 are locations that, when paired with locations surrounding thedeltoid muscle 20, the teres major muscle 25 and the latissimus dorsimuscle 30, can produce an electrical potential signal that is related tothe conventional signal measured between two quadrants. Morespecifically, the signal from these pairs of points on the left armcorrelates with the QRS complex associated with the contraction of theventricles.

Thus, by placing one electrode on the wrist 5, triceps muscle 10 or thebrachialis muscle 15 and a second electrode on the deltoid muscle 20,the teres major muscle 25 or the latissimus dorsi muscle 30, it ispossible to detect the action potential of the heart and thus an ECGsignal. The electrodes are preferably located near the central point ofthe deltoid and tricep muscles, are spaced approximately 130 mm and moreparticularly 70-80 mm apart and tilted at approximately 30-45 degreestoward the posterior of the arm from the medial line, with 30 degreesbeing most preferred. While certain specific preferred locations on ornear the left arm have been described herein as being related to theelectropotential of the second aspect of the ECG signal, it should beappreciated those locations are merely exemplary and that otherlocations on or near the left arm that are related to theelectropotential of the second aspect of the ECG signal may also beidentified by making potential measurements. It is further to bespecifically noted that the entire lower arm section 5′ is identified asproviding the same signal as wrist 5. Referring now to FIG. 2C, fourspecific pairs of operative locations are illustrated, having twolocations on the deltoid 20 and two locations on the various aspects ofthe tricep 10. It is to be noted that the dashed lines between thelocations indicate the operative pairings and that the solid and whitedots represent the relative aspects of the ECG signal obtainable atthose locations. Four possible combinations are shown which provide twoaspects of the ECG signal. An inoperative pair, 13 is illustrated toindicate that merely selecting particular muscles or muscle groups isnot sufficient to obtain an appropriate signal, but that carefulselection of particular locations is required.

In another embodiment, pairs of locations on or near the right arm forplacing electrodes to detect an ECG signal are identified. Referring toFIGS. 3A and 3B, the base of the trapezius 35, pectoralis 40 and deltoid20 are locations that are related to the electropotential of the secondaspect of the ECG signal, meaning that those locations are at apotential related to the heart's conventionally defined right sideaction potential. Tricep 10, especially the lateral head area thereof,and bicep 45 are locations that are related to the electropotential of afirst aspect of the ECG signal, meaning that those locations are at apotential related to the heart's conventionally defined left side actionpotential, even though those locations are in quadrant III. Thus, as wasthe case with the left arm embodiment described above, by placing oneelectrode on the tricep 10 and a second electrode on the deltoid 20, itis possible to detect the action potential of the heart and thus an ECGsignal. Again, while certain specific preferred locations on or near theright arm have been described herein as being related to theelectropotential of the first aspect of the ECG signal, it should beappreciated that those locations are merely exemplary and that otherlocations on or near the right arm that are related to theelectropotential of the first aspect of the ECG signal may also beidentified by making potential measurements.

Referring now to FIGS. 3C, 3D and 3E, a series of electrode pairlocations are illustrated. In FIGS. 3C and 3D, the conventionallydefined sagittal plane 2 and transverse plane 3 are shown in chain linegenerally bisecting the torso. Each of the operative pairs areidentified, as in FIG. 2C by solid and white dots and chain line.Inoperative pairs are illustrated by X indicators and chain line. Aspreviously stated, inoperative pairs are illustrated to indicate thatmere random selection of locations, or selection of independent muscleor muscle groups is insufficient to locate an operative pair oflocations. The specific locations identified as within the knownoperative and preferred embodiments are identified in Table 4 asfollows:

Reference Letter First Location (White) Second Location (Solid) A TricepDeltoid B Tricep Deltoid (top) C Right Trapezius Left Trapezius D LowerExternal Oblique Upper External Oblique E Upper External Oblique LowerPectoralis F Latissimus Dorsi Upper External Oblique G Upper ExternalOblique Upper External Oblique H Gluteus Maximus Lower External ObliqueI Inguinal Ligament Lower External Oblique J Lower Lateral ObliqueRectus Femoris JJ Inguinal Ligament Rectus Femoris K Rhomboid MajorLatissimus Dorsi L Latissimus Dorsi Latissimus Dorsi LL ThoracumbularFascia Latissimus Dorsi M Left Pectoralis Deltoid N Latissimus DorsiUpper External Oblique O Lower Trapezius Right Lower Trapezius Left PPectoralis Left Pectoralis Left Q Right Thigh Left Thigh R Right BicepRight Pectoralis S Right Inguinal Ligament Left External Oblique T UpperExternal Oblique Left Arm U Gluteus Maximus Right Gluteus Maximus Left

Similarly, it should be understood that the present invention is notlimited to placement of pairs of electrodes on the left arm or the rightarm for measurement of ECG from within quadrants I or III, as suchlocations are merely intended to be exemplary. Instead, it is possibleto locate other locations within a single quadrant. Such locations mayinclude, without limitation, pairs of locations on the neck, chest sideand pelvic regions, as previously described, that are inequipotentialwith respect to one another Thus, the present invention should not beviewed as being limited to any particular location, but instead hasapplication to any two inequipotential locations within a singlequadrant.

One of the primary challenges in the detection of these signals is therelatively small amplitudes or differences between the two locations.Additionally, these low amplitude signals are more significantly maskedand/or distorted by the electrical noise produced by a moving body, aswell as the noise produced by the device itself. Noise, in this context,refers to both contact noise created by such movement and interaction ofthe body and device, as well as electronic noise detected as part of thesignal reaching the sensors. An important consideration for eliminatingnoise is increasing the differentiation between the desired signal andthe noise. One method involves increasing signal strength by extendingone sensor or sensor array beyond the arm, to the chest or just past theshoulder joint. Consideration must be given with sensor placement to twocompeting desirable outcomes: increased signal strength/differentiationand compactness of the sensor array or footprint. The compactness is, ofcourse, closely related to the ultimate size of the device which housesor supports the sensors. Alternative embodiments, as described moreparticularly herein, include arrangements of sensors which strive tomaintain a compact housing for the device while increasing distancebetween the sensors by incorporating a fly-lead going to a sensorlocation point located some short distance from the device itself, suchas on the left shoulder, which is still within quadrant I, or even tothe other arm. The system further includes an electronic amplificationcircuit to address the low amplitude signal.

Referring to FIG. 4, a block diagram of circuit 100 for detecting an ECGsignal and for calculating other heart parameters such as heart ratetherefrom is shown. Circuit 100 may be implemented and embodied in awearable body monitoring device such as the armband body monitoringdevice described in U.S. Pat. No. 6,605,038 and U.S. application Ser.No. 10/682,293, owned by the assignee of the present invention, thedisclosures of which are incorporated herein by reference. AddressingFIG. 4 from left to right, circuit 100 includes electrodes 105A and105B, one of which is connected to a location as described herein thatis related to the electropotential of the first aspect of the ECGsignal, the other of which is connected to a location on the body thatis related to the electropotential of the second aspect of the ECGsignal, even if electrodes 105A and 105B are placed within a singlequadrant. The interface between the skin and first stage amplifier 115is critical as this determines how well the heart rate signal isdetected. Electrode contact impedance and galvanic potential areimportant design consideration when designing the first stage amplifierblock and the associated bias/coupling networks.

Electrodes 105A and 105B are held against the skin to sense therelatively small voltages, in this case on the order of 20 μV,indicative of heart muscle activity. Suitable electrodes include RedDot™ adhesive electrodes sold by 3M, which are disposable, one-time useelectrodes, or known reusable electrodes made of, for example, stainlesssteel, conductive carbonized rubber, or some other conductive substrate,such as certain products from Advanced Bioelectric in Canada. It shouldbe noted that unlike the Advanced Bioelectric development, most currentreusable electrodes typically have higher coupling impedances that canimpact the performance of circuit 100. Thus, to counteract this problem,a gel or lotion, such as Buh-Bump, manufactured by Get Rhythm, Inc. ofJersey City, N.J., may be used in conjunction with electrodes 105A and105B when placed in contact with the skin to lower the skin's contactimpedance. In addition, the electrodes 105 may be provided with aplurality of microneedles for, among other things, enhancing electricalcontact with the skin and providing real time access to interstitialfluid in and below the epidermis. Microneedles enhance electricalcontact by penetrating the stratum corneum of the skin to reach theepidermis. It is beneficial to make the ECG signal measurements at aposition located below the epidermis because, as noted above, thevoltages are small, on the order of 20 μV, and the passage of the signalthrough the epidermis often introduces noise artifacts. Use ofmicroneedles thus provides a better signal to noise ratio for themeasured signal and minimizes skin preparation. Such microneedles arewell known in the art and may be made of a metal, silicon or plasticmaterial. Prior art microneedles are described in, for example, in U.S.Pat. No. 6,312,612 owned by the Procter and Gamble Company. Based on theparticular application, the number, density, length, width at the pointor base, distribution and spacing of the microneedles will vary. Themicroneedles could also be plated for electrical conductivity,hypoallergenic qualities, and even coated biochemically to alsoprobe/sense other physiological electro-chemical signal or parameterswhile still enhancing the electrical potential for ECG measurement. Themicroneedles may also be adapted to simultaneously sample theinterstitial fluid through channels that communicate with micro levelcapillary tubes for transferring fluid in the epidermis for sensingelectrically, chemically, or electro chemically. Microneedles furtherenhance the ability of the electrodes to remain properly positioned onthe skin during movement of the user. The use of microneedles, however,may limit the ability of the sensors to be mounted on a larger device orhousing, as the weight of the larger device may cause the microneedlesto shear during movement. In such instances, the microneedle-enhancedsensor may be separately affixed to the body as shown in severalembodiments herein. Use of adhesives to supplement the use ofmicroneedles, or alone on a basic sensor is also contemplated. As willbe discussed further herein, the use of materials of differentflexibilities or incorporating a elastomeric or spring-likeresponsiveness or memory may further improve sensor contact andlocational stability.

In certain circumstances, it is important for a clinician or otherobserver of the user to determine whether the device has remained inplace during the entire time of use, for the purposes of compliance witha protocol or other directive. The use of certain adhesives, oradhesives coupled with plastic or cloth in the nature of an adhesivebandage may be utilized to affix the device to the skin and which wouldbe destroyed or otherwise indicate that removal of the device hadoccurred or been attempted.

For a wearer to accurately or most affectively place the system on theirarm, it may be at least necessary to check that the device is situatedin a proper orientation and location, even if the desired location ofthe electrodes includes an area with significant tolerance with respectto position. In one particular embodiment of the present invention, adevice having an array of electrodes 105, such as armband monitoringdevice 300 described above, is placed in an initial position on the bodyof the wearer, with each of the electrodes 105 is in an initial bodycontact position. The device then makes a heart rate or other heartrelated parameter measurement as described above, and compares themeasured signal to a what would be an expected signal measurement for aperson having the physical characteristics of the wearer, which had beenpreviously input into the system as more fully described herein, such asheight, age, weight and sex. If the measured signal is meaningfully moredegraded, as determined by signal to noise ratio or ratio of beat heightto noise height, than the expected signal, which would be a presetthreshold value, the device gives a signal, such as a haptic, acoustic,visual or other signal, to the wearer to try a new placement positionfor the device, and thus a new contact position for the electrodes 105.A second measurement is then made at the new position, and the measuredsignal is compared to the expected signal. If the measured signal ismeaningfully more degraded than the expected signal, the new positionsignal is given once again to the wearer. This process is repeated untilthe measured signal is determined by the device to be acceptable. Whenthe measured signal is determined to be acceptable, the device generatesa second success signal that instructs the wearer to leave the device inthe current placement location. The device may initiate this operationautomatically or upon manual request.

Circuit 100 also includes bias/coupling network 110, shown as two boxesin FIG. 4 for convenience, and first stage amplifier 115. As will beappreciated by those of skill in the art, the approximately 20 μVpotential difference signal that is detected by electrodes 105A and 105Bwill, when detected, be biased too close to the limits of first stageamplifier 115, described below. Thus, bias/coupling network 110 isprovided to increase the biasing of this signal to bring it within theallowable input range for first stage amplifier 115.

Two approaches to providing bias current for the amplifier inputs areshown in FIGS. 5A and 5B, as will be described more fully herein.Preferably, bias/coupling network 110 will move the bias of the signalup to the middle range of first stage amplifier 115. In the preferredembodiment, as described below, first stage amplifier 115 is a rail torail amplifier having rails equal to 0 V and 3 V. Thus, bias/couplingnetwork 110 will preferably increase the bias of the voltage potentialdifference signal of electrodes 105A and 105B to be approximately 1.5 V.

Although not specifically described, the bias/coupling network can bedynamic, in that adjustments can be made based upon the signals beingproduced when the device is first engaged, or under changing contextconditions. This dynamic capability would also accommodate individualdifferences in amplitude for different placements of similar devicesbecause of user size or other physical characteristics. Experimentationhas shown some degree of variation on signal strength based upondistance. Furthermore, changes in signal are expected based on theamount of motion the device is doing relative to the arm, the flexing ofthe electrodes and their contact with the skin, contractions andrelaxations of the muscles below or around the skin contact points andthe movement of the body.

Preferably, bias/coupling network 110 employs capacitive input couplingto remove any galvanic potential (DC voltage) across electrodes 105A and105B when placed on the body that would force the output of first stageamplifier 115 outside of its useful operating range. In addition, thenon-zero input bias current of first stage amplifier 115 requires acurrent source/sink to prevent the inputs from floating to the powersupply rails. In one embodiment, bias/coupling network 110 may take theform shown in FIG. 5A. In the embodiment shown in FIG. 5A, bias-couplingnetwork 110 includes capacitors 120A and 120B connected to electrodes105A and 105B, respectively, which are in the range of 0.1 μF to 1.0 μFand resistors 125A and 125B connected as shown, which have a value ofbetween 2 MΩ to 20 MΩ. As will be appreciated, resistors 125A and 125Bprovide the bias current for first stage amplifier 115 following Ohm'slaw, V=IR. In addition, bias/coupling network 110 includes capacitors130A, 130B and 130C, the purpose of which is to filter out ambient RFthat may couple to the high impedance lines prior to the amplifier inthe circuit. Preferably, capacitors 130A, 130B and 130C are on the orderof 1000 pF. A 1.5 volt mid-supply reference voltage 122 is furtherprovided to keep the signals centered in the useful input range of theamplifiers.

Referring to FIG. 5B, an alternative embodiment of bias/coupling network110 is shown in which resistors 125A and 125B have each been replacedwith two diodes connected back to back, shown as diodes 135A and 140Aand 135B and 140B, respectively. In this configuration, with no inputsignal applied from electrodes 105A and 105B, diodes 135A, 135B, 140Aand 140B provide the currents required by first stage amplifier 115 andbias each input slightly away from the 1.5 V reference 122. When asignal is applied to electrodes 105A and 105B, the very small change involtage, typically 20 μV, results in very small changes in currentthrough the diodes, thereby maintaining a high input impedance. Thisconfiguration permits exponentially higher currents to bias first stageamplifier 115 quickly when a large adjustment is necessary, such as isthe case during initial application of electrodes 105A and 105B to thebody. An added benefit of such a configuration is the increasedelectro-static discharge protection path provided through the diodes toa substantial capacitor (not shown) on the 1.5 V reference voltage 122.In practice, this capacitor has a value between 4.7 and 10 μF and iscapable of absorbing significant electro-static discharges.

Referring again to FIG. 4, the purpose of first stage amplifier 115 isto amplify the signal received from bias/coupling network 110 before itis filtered using filter 150. The main purpose of filter 150 is toeliminate the ambient 50/60 Hz noise picked up by electrodes 105A and105B when in contact with the body of the user. This noise is oftenreferred to as mains hum. The filter 150 will add some noise, typicallyin the range of 1 μV, to the signal that is filtered. Therefore, thepurpose of first stage amplifier 115 is to amplify the signal receivedfrom bias/coupling network 110 before it is filtered using filter 150 sothat any noise added by the filtering process will not overwhelm thesignal. As will be appreciated, since the signal output by bias/couplingnetwork 110 is on the order of 20 μV, filtering with filter 150 withoutfirst amplifying the signal using first stage amplifier 115 will resultin a signal that is overwhelmed by the noise added by filter 150. Thus,first stage amplifier 115 is used to amplify the signal with a gainpreferably between 100 and 10,000, most preferably 255.

A suitable example of first stage amplifier 115 is shown in FIG. 5C,which includes programmable gain amplifier 116, which is preferablymodel AD627 sold by Analog Devices, Inc. of Norwood, Mass. or modelLT1168 sold by Linear Technology Corporation of Milpitas, Calif. Thegain of these amplifiers is determined by a gain select resistor coupledto appropriate inputs of the amplifier. Thus, an input multiplexer 117,such as the model ADG608 multiplexer sold by Analog Devices, Inc. may beused to selectively switch in and out one of a number, preferably 8, ofgain select resistors for the programmable gain amplifier used for firststage amplifier 115 during a testing period to determine an appropriategain select resistor for the amplifier. Once a candidate gain isdetermined using the input multiplexer in a testing mode, a single fixedresistor for gain can be selected for use in connection with theprogrammable gain amplifier used as first stage amplifier 115.

Key parameters in selecting an amplifier for first stage amplifier 115are input bias current, input offset current, and input offset voltage.Input bias current multiplied by the input impedance of thebias/coupling network gives the common-mode input offset voltage of thepositive and negative inputs to first stage amplifier 115. Care must betaken to keep the inputs of first stage amplifier 115 far enough fromthe power supply rails to prevent clipping the desired output signal. Aswith the bias/coupling network, an alternative design might include acircuit which was able to dynamically limit the input voltage based uponthe type of activity, such as power on, initial attachment to the arm,or certain high-motion activities so that the input voltage under normalconditions would be optimum. As one skilled in the art would appreciate,some clipping can be acceptable. Algorithms for detecting heart rate orother heart parameters can work in the presence of some amount ofclipping, assuming that the signal to noise ratio remains relativelyhigh.

The input offset current parameter multiplied by the bias impedancegives the differential input voltage that is applied to first stageamplifier 115. This differential voltage is in addition to the inputoffset voltage parameter that is inherent in the amplifier, and thetotal input offset is simply the sum of the two. The total differentialinput voltage multiplied by the gain determines the output offset.Again, care must be taken to keep the output signal far enough from thepower supply rails to prevent saturation of the amplifier output. As anexample, a bipolar amplifier such as the model AD627 described above hasan input bias current of 10 nA, an input offset current maximum of 1 nA,and an input offset voltage of 150 μV (all values are worst casemaximums at 25° C.). In order to keep the common-mode input offset toless than 0.5 V, the bias impedance must be no more than 0.5 V/10 nA=50MΩ. However, the input offset current dictates that in order to maintaina maximum 0.5 V output offset voltage, one must provide an inputimpedance of no more than 0.5 V/gain/1 nA. For a gain of 100, thisresolves to 5 MΩ. For a gain of 500, this resolves to 1 MΩAnothercandidate amplifier for use as first stage amplifier 115 is the TexasInstruments Model INA321 programmable gain amplifier, which has FETinputs. This amplifier has an input bias current of 10 pA and an inputoffset current of 10 pA (max). In order to keep the common-mode inputoffset to less than 0.5 V, one must provide an impedance of no more than0.5 V/10 pA=50 GΩ. However, the input offset current dictates that inorder to maintain a maximum 0.5 V output offset, one must provide aninput impedance of no more than 0.5 V/gain/10 pA. For a gain of 100,this resolves to 500 MΩ. For a gain of 1,000, this resolves to 50 MΩ.

As an alternative, as will be appreciated by those of skill in the art,first stage amplifier 115 may be implemented in a network of low costdiscrete op-amps. Such an implementation will likely reduce the cost andpower consumption associated with first stage amplifier 115. As willalso be appreciated by those of skill in the art, the same analysis ofamplifier input bias current, output saturation, and input bias/couplingapplies to such an alternative implementation.

Referring again to FIG. 4, filter 150 is a bandpass network preferablyincluding separate low-pass and high-pass filter sections. The purposeof the low-pass filter section is to eliminate the ambient 50/60 Hznoise picked up by electrodes 105A and 105B when in contact with thebody. Preferably, a multi-pole filter is used to achieve a high degreeof attenuation. The high-pass filter section eliminates the DC wander ofthe signal baseline due to galvanic effects in electrodes 105A and 105B,allowing the heart beat spikes forming a part of the measured ECG signalto be more easily detected by hardware or software means.

In one embodiment, filter 150 includes switched capacitor low-pass andhigh-pass filters with adjustable cutoff frequencies to allow forexperimentation. Such a filter 150 may be constructed using the modelLTC1164_(—)6 low-pass filter chip sold by Linear Technology Corporationfollowed by a model LTC 1164 high-pass filter chip also sold by LinearTechnology Corporation, which chips provide an eighth order ellipticalfilter with very sharp cutoff characteristics. Experimentation with thisimplementation has shown that a low-pass cutoff frequency of 30 Hz and ahigh-pass cutoff frequency of between 0.1 Hz and 3 Hz worked well.Although allowing for flexibility, this implementation is relativelyexpensive and was found to consume a significant amount of power.

An alternative implementation for filter 150 is shown in FIG. 6. Thecircuit shown in FIG. 6 implements a sixth order active filter usingdiscrete op-amps in a multiple feedback topology. The circuit shown inFIG. 6 consumes less current and costs significantly less than theswitched capacitor design described above. Values for the resistors andcapacitors shown in FIG. 6 may be selected using a software tree packagesuch as the FilterPro package provided by Texas Instruments. As will beappreciated by those of skill in the art, the different filter styles,such as Butterworth, Bessel, and Elliptic, may be implemented simply bychanging component values. The FilterPro package also providesinformation that is useful in selecting the amplifiers shown in FIG. 6,including necessary bandwidth for each stage. Suitable amplifiersinclude the models TLV2764 and OPA4347 quad amplifiers sold by TexasInstruments Incorporated of Dallas, Tex. The three-stage (first threeop-amps) sixth order filter forming part of the circuit shown in FIG. 6provides adequate 60 Hz filtering, thereby allowing the fourth op-amp inthe circuit to be used for second stage amplifier 155 shown in FIG. 4and described below. In addition, the R-C Network shown in FIG. 6 thatcouples the third stage op-amp of the low-pass filter to the fourthop-amp (the gain stage) provides a high-pass network which eliminates DCdrift as described above.

Referring again to FIG. 4, circuit 100 includes second stage amplifier155 for amplifying the signal output by filter 150 to a level that canbe directly sampled by analog to digital converter 160. Specifically, ifthe gain of first stage amplifier 115 is between 100 and 10,000, theamplitude of the signal output by filter 150 will be in the range of 2mV to 200 mV. Preferably, the gain of first stage amplifier 115 is 500,and therefore the amplitude of the signal output by filter 150 will beon the order of 10 mV. In order to allow for a higher samplingresolution by analog to digital converter 160, second stage amplifier155 is used to further amplify the signal. Preferably, second stageamplifier has a gain on the order of 30, and therefore would amplify the10 mV signal in the preferred embodiment to a 300 mV signal. However,the gain of second stage amplifier 155 may also be on the order of 10 to100. As was the case with first stage amplifier 115, a programmable gainamplifier may be used for second stage amplifier 155. Alternatively, asdescribed above, the unused (fourth) op-amp in the filter 150implementation shown in FIG. 6 may be used for second stage amplifier155.

Analog to digital converter 160 converts the analog waveform output bysecond stage amplifier 155 into a digital representation that can thenbe processed by one or more algorithms, as described more fully herein,to determine heart related parameters, such as heart rate, therefrom.Analog to digital converter 160 may be implemented using a 12 bit analogto digital converter with a 3 V reference at 32-256 samples per second.Such a device is integrated into the Texas Instruments MSP430F135processor. Analog to digital converter 160 is connected to centralprocessing unit 165, which reads the converted digital signal andperforms one of the following functions: (i) it stores the raw digitalsignal to memory, such as flash or SRAM, for subsequent analysis; (ii)it stores a number of raw digital signals to memory and subsequentlytransmits them, wired or wirelessly, to a remote computer for analysisas described herein and/or display, such as display in real time; or(iii) it processes the raw digital signals using algorithms describedherein provided on central processing unit 165 to determine heartrelated parameters, such as the timing and various sizes of heart beats,heart rate, and/or beat-to-beat variability. With respect to this lastfunction, central processing unit 165 may, once heart beats and/or heartrate has been determined, perform a variety of tasks such as blink anLED for each beat or store heart rate information to memory. Optionally,central processing unit may provide operational control or, at aminimum, selection of an audio player device 166. As will be apparent tothose skilled in the art, audio player 166 is of the type which eitherstores and plays or plays separately stored audio media. The device maycontrol the output of audio player 166, as described in more detailbelow, or may merely furnish a user interface to permit control of audioplayer 166 by the wearer.

These functions can also be performed independently in sequence. Forexample, the data can be stored in real time in a data storage mediumwhile being simultaneously analyzed and output. Subsequent processes canallow the system to retrieve earlier stored data and attempt to retrievedifferent information utilizing alternative algorithmic techniques orfilters. Additionally, data from different points in the filtrationprocess, described above, can be simultaneously stored and compared orindividually analyzed to detect signal information which is lost atcertain points in the process.

Referring to FIG. 7, alternate circuit 200 for measuring an ECG signalis shown in which an array of multiple electrodes 105, for example fourelectrodes 105A through 105D, are used. The electrodes 105 in thisembodiment are grouped in pairs and, as was the case with circuit 100shown in FIG. 4, one electrode of each pair is placed in a location thatis related to the electropotential of the right side of the ECG signaland the other electrode in each pair is placed in a location that isrelated to the electropotential of the left side of the ECG signal. Thefirst electrodes in each pair may be placed in locations close to oneanother to attempt to get a good signal form a particular generallocation, or may be placed in locations removed from one another, asillustrated in the particular embodiments described with more detailbelow, to pick up signals from different locations. The secondelectrodes in each pair may be similarly placed. Each pair of electrodes105 is connected to bias/coupling network 110 as described above, andthe output is connected to a first stage amplifier 115 as describedabove. In the embodiment shown in FIGS. 7, 8A-D and 8F, the output ofeach first stage amplifier 115 is fed into summation circuit 170, whichfor example may be a resistor network Summation circuit 170 adds theoutputs of the first stage amplifiers 115 together. The summed signal isthen passed through filter 150, second stage amplifier 155, and toanalog to digital converter 160 and central processing unit 165 asdescribed above.

It is to be specifically noted that the circuitry may be implemented ina minimal cost and component embodiment, which may be most applicable toa disposable application of the device. In this embodiment, theapparatus is not provided with a processor, only electrically separatedelectrodes for picking up a voltage difference, a gating mechanism fordifferentially passing current associated with voltage spikes, such asQRS signals and a mechanism for displaying characteristics of the passedthrough current. This apparatus may be powered by motion, battery, orsolar power. Another option is to power the apparatus directly from thevoltage potentials being measured. The display mechanism may bechemical, LCD or other low power consumption device. The voltage spikescharge up a capacitor with a very slow trickle release; a simple LEDdisplay shows off the charge in the capacitor. In another embodiment, asimple analog display is powered by the battery. The simple apparatusutilizes digital processing but no explicit processor; instead a simplecollection of gates, threshold circuitry and accumulator circuitry, aswould be apparent to one skilled in the art, based upon the descriptionsabove, controls the necessary preprogrammed logic.

The implementation shown in FIGS. 7 and 8A-F, which utilize an array ofelectrodes 105, is particularly useful and advantageous due to the factthat the signals detected by electrodes 105 can at times be saturated bymuscle activity of the body, such as muscle activity in the arm in anembodiment where electrodes 105 are placed on locations of the arm. Theheart beat related portion of the signals detected by electrodes 105 arecoherent, meaning highly correlated, while the muscle activity noiseportions of the signals tend to be incoherent, meaning not correlated.Thus, because of this coherent/incoherent nature of the differentportions of signals, when the signals generated by electrodes 105 aresummed, subtracted, averaged, multiplied or the like, by summationcircuit 170, the heart beat related components will add to one anotherthereby producing better heart beat spikes having a higher signal tonoise ratio, while the muscle noise related components will tend to washor cancel one another out because the “hills” and “valleys” in thosesignals tend to be off phase from one another. The result is a strongerheart beat related signal with less muscle related noise.

FIGS. 8A through 8F illustrate alternative embodiments of the systemincorporating multiple electrodes shown in FIG. 7. FIG. 8A illustrates,three electrodes 105B-F interchangeably routed by switches 111 to any ofthe first stage differential amplifier 115 inputs to allow variouscombinations of electrode subtractions and additions. This arrangementassumes that one electrode will always be treated in the positive sense.FIG. 8B illustrates an arrangement similar to FIG. 8A, however, a 3×3switch matrix 112 is utilized rather than the discrete switches shown inFIG. 8A. FIG. 8C illustrates a 4×4 switch matrix 113, which allows fullcontrol of electrode pair addition/subtraction and is the most simpleconceptually. In some embodiments, the functionality of the switchmatrix 113 may be reduced to permit only certain pairings in order toobtain a cleaner signal. FIG. 8D illustrates a 6×4 switch matrix 114,which allows full control of electrode pair addition/subtraction andpermits the selection of two pairs from the full suite of electrodes.FIG. 8D includes additional electrodes 105E-F to illustrate theselectability of three full pairs of such electrodes. As with theembodiment shown in FIG. 8C, the functionality of the switch may bereduced to permit only certain pairings. This could conceptually beexpanded to as many electrodes as desired. FIG. 8E illustrates anembodiment that provides electrode shielding, and the individual pairsof electrodes can be sampled and then summed and/or subtracted duringsubsequent analysis, the strongest pair may simply be chosen or theaverage may be taken of an array of signals. This arrangement can alsorequire 50-60 Hz filtering and higher first stage amplifier gains tokeep the signal to noise ratio high. FIG. 8F illustrates an embodimentin which the CPU controls the gain of the first stage amplifier throughAGC circuits 167, enabling the system to adjust for poor electrodeplacement or subjects with weaker ECG signals. These embodiments permitthe selection of the strongest pair or best signal from of amultiplicity of pairs of electrodes for analysis. This can beaccomplished according to several methodologies in addition to meresignal strength. These include the analysis of all the pairs andcombination of the signals or calculation of an average of all of thesignals or the identification of the most distorted signal, consideringmuscle artifact noise or the like, and utilizing it as a filter signalto be subtracted from the identified best signal.

There are multiple sources of noise that can affect the amplified signalthat is input into analog to digital converter 160 shown in FIGS. 4, 7and 8A-F. For example, as described above, mains hum and DC wander noisecan effect the signal. In the embodiments shown in FIGS. 4, 7 and 8A-F,this noise is removed using filter 150. In an alternate embodiment,rather than using a hardware solution like filter 150 to remove the50/60 Hz mains hum and/or DC wander noise from the voltage potentialdifference signal received from electrodes 105, some or all of thisnoise can be filtered out of the signal, after being digitized by analogto digital converter 160, using known software techniques implemented insoftware residing either on CPU 165 forming a part of a body monitoringdevice or on a separate computer that receives the digitized signal. Inthis embodiment, filter 150 would be eliminated and only a singleamplifier having a gain on the order of 500 to 2500 such as first stageamplifier 115 would be used in circuit 100 or 200. A two stage amplifiermay also be utilized, having first stage gain of 50-500 and a secondstage gain of 10-50. These steps (in either the hardware or softwareimplementations), in effect remove components of the signal havingfrequencies that are considered to be too high or too low to constitutea heart related signal, with a typical ECG signal having a frequency inthe range of 0.5-4 Hz.

The system is specifically designed to minimize the processing timedelays and interruptions created by noise being processed and subtractedor filtered from the primary signal. As noise is processed and consumingprocessor resources, data must be stored and processed at a later time.It is important to return as quickly as possible to contemporaneousmonitoring so as to avoid the build up of a backlog of data. The systemutilizes a plurality of measurement techniques, such as described aboveto quickly identify and extract the primary signal and rapidly return toreal time monitoring. Most particularly, the circuitry is designed tominimize DC wander within three beats of the heart.

In addition, another source of noise that may affect the signal inputinto analog to digital converter 160 is muscle noise caused by theelectrical activity of muscles. Electromyography, or EMG, is ameasurement of the electrical activity within muscle fibers, which isgenerally measured actively, but could also be measured passively,according to the method of subtraction or filtering of the mostdistorted signal described above, because it is affected most by muscleartifact and/or has very little if not any signal relating to the heartrelated electrical activity. While a subject is in motion, electrodes105 for measuring ECG may also simultaneously pick up and measure EMGsignals. Such contemporaneously measured EMG signals are noise to theECG signal. Thus, according to an aspect of the present invention, ECGsignal measurement can be improved by using separate electrodes tospecifically measure an EMG signal, preferably from body locations thathave a minimal or difficult to detect ECG signal. This separatelymeasured EMG signal may then be used to reduce or eliminate EMG noisepresent in the separately and contemporaneously measured ECG signalusing various signal processing techniques. In many cases, the EMGsignal's amplitude may so overwhelm that ECG signal that eitherfiltering or utilizing the above-described method may not result in ausable ECG signal. In these events, the use of a non-electrode sensorcould be utilized in conjunction with electrodes in order to detect therelatively quiet ECG signal. This sensor may even replace the beatdetection if it detected ECG peaks when the primary electrical signalclips, gets oversaturated or overwhelmed by the EMG signal. An examplesensor is a micro-Doppler system, either as a single pick-up or anarray, designed to pick up the mechanical rushing of blood or the like,past the Doppler signal, creating a pulse wave in which the peak couldbe recognized and timed as a beat. This embodiment could be tuned to aspecific location or utilize an array of different sensors tuned todifferent depths in order to optimize and locate the best signal foreach user. This array could also be utilized, through monitoring ofdifferent signals and signal strength, to locate the device at the bestposition on the arm through well known audible or visual feedbackmechanisms. The device could also be tuned to certain individualcharacteristics detected over an introductory period of evaluation ortuned dynamically over a period of time. Under certain high noisecircumstances, the mechanical signal might be substituted for theelectrical ECG signal as part of the calculations. In order to make themechanical and electrical wave align, timing and phase shift differenceswould have to be calculated and factored into the peak or beatrecognition algorithm. This system could be also utilized for detectionand measurement of pulse transit time, or PTT, of the wearer, asdescribed more fully herein, allowing relative and/or absolutemeasurement of blood pressure could be derived or calculated.

Pulse transit time, or PTT, is the time that it takes a pulse pressurewaveform created by a heart beat to propagate through a given length ofthe arterial system. The pulse pressure waveform results from theejection of blood from the left ventricle of the heart and moves throughthe arterial system with a velocity that is greater than the forwardmovement of the blood itself, with the waveform traveling along thearteries ahead of the blood. PTT can be determined by measuring the timedelay between the peak of a heart beat, detected using the R-wave of anECG signal and the arrival of the corresponding pressure wave at alocation on the body such as the finger, arm, or toe, measured by adevice such as a pulse oximeter or other type of pressure detector. Asblood pressure increases, more pressure is exerted by the arterial wallsand the velocity of the pulse pressure waveform increases. The velocityof the pulse pressure waveform depends on the tension of the arterialwalls; The more rigid or contracted the arterial wall, the faster thewave velocity. As a result, for a fixed arterial vessel distance, as PTTincreases and pulse pressure waveform velocity decreases, blood pressuredecreases, and as PTT decreases and pulse pressure waveform velocityincreases, blood pressure increases. Thus, PTT can be measured and usedto indicate sudden changes in real-time blood pressure.

In one embodiment, the same armband device includes the ability todetect the ECG signal and in conjunction with a micro Doppler arrayagainst the body, together create the PTT measurement. An aspect of thepresent invention relates to the measurement and monitoring of PTT.Specifically, the time of a heart beat peak can be determined using anECG signal using electrodes 105 as described herein. The time of thearrival of the corresponding pressure wave at a given location on thebody can be measured using any one of a number of pressure sensors. Suchpressure sensors may include, but are not limited to, pulse oximeters,Doppler arrays, single piezoelectric sensors, acoustic piezoelectricsensors, fiber optic acoustic sensors, blood volume pressure or BVPsensors, optical plethysmographic sensors, micropower impulse radardetectors, and seismophones. According to a preferred embodiment of thepresent invention, PTT is measured and monitored to indicate changes inblood pressure using armband body monitoring device 300 that is providedwith one or more of the pressure sensors described above. Thus, in thisembodiment, PTT is measured in a single device that obtains an ECGsignal from the upper arm and that measures the arrival of the pulsepressure waveform at a location on the upper arm. Alternatively, thepressure sensor may be located separately from armband body monitoringdevice 300 at a different location, such as the finger or wrist, withthe information relating to the arrival time being transmitted toarmband body monitoring device 300 for calculation. This calculation mayalso be made at the finger product, or other third product, or sharedbetween any combination of the above. Communication between each devicecan be provided in a wired or wireless embodiment, or transmittedthrough the skin of the wearer, as is well known to those skilled in theart.

An alternative embodiment includes the incorporation of third partydevices, not necessary worn on the body, collect additional data to beutilized in conjunction with heart parameter data or in support thereof.Examples include portable blood analyzers, glucose monitors, weightscales, blood pressure cuffs, pulse oximeters, CPAP machines, portableoxygen machines, home thermostats, treadmills, cell phones and GPSlocators. The system could collect from, or in the case of a treadmillor CPAP, control these devices, and collect data to be integrated intothe streams for real time or future derivations of new parameters. Anexample of this is a pulse oximeter on the user's finger could helpmeasure PTT and therefore serve a surrogate reading for blood pressure.Additionally, a user could utilize one of these other devices toestablish baseline readings in order to calibrate the device.

In one specific embodiment, electrodes 105 may be placed on the deltoidmuscle and the triceps muscle of the left arm in order to measure an ECGsignal, which will likely contain muscle related noise, and separateelectrodes 105 may be placed one each on the triceps muscle or one onthe triceps muscle and one on the brachialis muscle for collecting anEMG signal having little or no ECG component, according to at least oneof the several embodiments of the device more fully described below.This EMG signal may then be used to process and refine the measured ECGsignal to remove the EMG noise as described herein. An example of such aconfiguration is armband body monitoring device 300 described below inconnection with the specific alternative embodiments of the device, andmore specifically FIG. 15, in which electrodes 105A and 105B wouldmeasure an ECG signal likely containing muscle related noise, andelectrodes 105C and 105D measure an EMG signal having little or no ECGcomponent.

Although muscle noise can be reduced using separate EMG sensors as justdescribed, it has been found that this noise, to a degree, often ends upremaining in the signal input into analog to digital converter 160despite efforts to eliminate or reduce such noise. The amplitude ofactual heart beat spikes, which comprise the QRS wave portion of the ECGsignal, in the collected signal may vary throughout the signal, and theremaining muscle noise may obscure a heart beat spike in the signal ormay itself look like one or more heart beat spikes. Thus, an aspect ofthe present invention relates to various processes and techniques,implemented in software, for identifying and reducing noise that ispresent in the digital signal output by analog to digital converter 160and identifying heart beats and heart beat patterns from that signal. Inaddition, there may be portions of the signal that, despite processingefforts, contain too much noise and therefore no discernable heartrelated signal. A further aspect of the present invention relates toprocess and techniques for dealing with such portions and interpolatingthe data necessary to provide continuous and accurate output.

According to a one embodiment of the present invention, the signal thatis output by analog to digital converter 160 may first undergo one ormore noise reduction steps using software residing on either CPU 165 oron a separate computer to which the signal has been sent. For example,in one possible noise reduction implementation, the signal is firstprocessed to identify each peak in the signal, meaning an increasingamplitude portion followed by a maximum amplitude portion followed by adecreasing amplitude portion. An example of such a peak is shown in FIG.9 and includes points A, B and C wherein the X axis is time and the Yaxis is signal strength or amplitude. For each identified peak, theheight of the peak (in units of amplitude) and the width of the peak (inunits of time) are then calculated. Preferably, the height for each peakis determined as follows: min (BY−AY, BY−CY), and the width for eachpeak is determined as follows: (CX−AX). In addition, a standard heightand width profile of a heart beat spike, comprising the QRS wave, isestablished and stored, and identified peaks present in the signal thatare outside of the stored profile are eliminated, meaning that thoseportions of the signal are marked to be ignored by further processingsteps because they constitute noise. In a preferred embodiment, thestandard height in the stored profile is approximately 400 points when a128 Hz analog to digital sampling rate is used and a 12-bit encoding ofthe signal is used and the standard width in the stored profile isapproximately 3 to 15 points when a 128 Hz analog to digital samplingrate is used and a 12-bit encoding of the signal is used. In oneparticular embodiment, the profile may constitute an adaptive heightand/or width that is stored and used for identifying spikes in thesignal that are to be eliminated, such as a height and/or width based ona percentage of the moving average of previous measurements. Inaddition, peaks in the signal that hit the maximum and minimum valuerails output by analog to digital converter 160 may be eliminated aswell. Peaks may also be eliminated from the signal if they wouldindicate an unlikely heart rate given the surrounding signal context,i.e., other peaks in close proximity that would result in a calculatedheart rate that is above a likely maximum value. Finally, noise can beremoved based on using additional sensors preferably provided with thebody monitoring device that implements circuit 100 shown in FIG. 4 orcircuit 200 shown in FIG. 7, including, but not limited to,accelerometers or other motion detecting sensors for detecting eithermotion or tension, audio sensors, or using time-spectrum signature ofmuscle noise.

FIGS. 7A through 7D illustrate the progressive steps of obtaining andextracting the ECG data and heart beats from the detected signal.Referring now to FIG. 7A, the detected signal 75 is illustrated inconjunction with a simultaneously recorded reference signal 76 of thesame heartbeat by a conventional ECG monitor. The detected signal 75 isessentially without notable features and the entire heart related signalis masked by noise. Most prevalent in FIG. 7A is 60 Hz mains hum 77,which is present in the reference signal as well. FIG. 7B illustratesthe same two signals after filtering with a 30 Hz filter. The referencesignal 76 reveals an essentially intact and unobscured ECG signal. Thedetected signal reveals some periodic features, but with minimalamplitude or signal strength. FIG. 7C illustrates the modification ofthe detected signal 75 after amplification. Reference signal 76 has notbeen modified. FIG. 7D illustrates only detected signal 75 afteradditional signal processing and identification of the peaks 77, asdescribed more fully herein.

Another method for eliminating noise is that of filtering the signal insoftware residing either on either CPU 165 or on a separate computer towhich the signal has been sent. In the preferred embodiment, thisfiltering consists of a non-linear filter designed to accentuatedifferences between noise and heartbeats. FIG. 7E shows the results ofapplying this filter. Detected signal 75 is illustrated in box 80 in anunfiltered state and in box 79 after filtering.

While these noise reduction steps are likely to remove a significantamount of noise from the signal received from analog to digitalconverter 160, it is likely that, notwithstanding this processing, therewill still be noise remaining in the signal. This noise makes the taskof identifying actual heart beat spikes from the signal for purposes offurther processing, such as calculating a heart rate or other heartrelated parameters, difficult. Thus, a further aspect of the presentinvention relates to various processes and techniques, again implementedin software residing on either CPU 165 or a separate computer, foridentifying heart beat spikes from the signal notwithstanding anyremaining noise. As will be appreciated, these processes and techniques,while preferably being performed after one or more of the noisereduction steps described above, may also be performed with any priornoise reduction steps having been performed.

As is well-known in the prior art, the Pan-Tompkins method uses a set ofsignal processing frequency filters to first pass only the signal thatis likely to be generated by heart beats, then proceeds todifferentiate, square and perform a moving window integration on thepassed signal. The Pan-Tompkins method is described in Pan, J. &Tompkins, W. J., “A Real-time QRS Detection Algorithm,” IEEETransactions on Biomedical Engineering, 32, 230-236 (1985), thedisclosure of which is incorporated herein by reference.

According to this aspect of the invention, areas in the signal output byanalog to digital converter 160 (with or without noise reduction asdescribed above) having excessive noise, i.e., too much noise topractically detect acceptable heart beat spikes from the signal, arefirst identified and marked to be ignored in the processing. This may bedone by, for example, identifying areas in the signal having more than apredetermined number of rail hits or areas in the signal within apredetermined time window, e.g., ¼ of a second, of two or more railhits. Next, the remaining areas, i.e., those not eliminated due to toomuch noise being present, referred to herein as the non-noise signal,are processed to identify acceptable heart beat spikes for use incalculating various heart parameters such as heart rate.

In one embodiment of the present invention, acceptable heart beat spikesare identified in the non-noise signal by first identifying and thencalculating the height and width of each peak in the non-noise signal asdescribed above. Next, the width of each peak is compared to apredetermined acceptable range of widths, and if the width is determinedto be within the acceptable range, the height of the peak is compared toan adaptive threshold height equal to 0.75 of the moving average of theheight of the previous peaks. Preferably, the acceptable range of widthsis 3 to 15 points when a 128 Hz analog to digital sampling rate is used,and represents a typical range of widths of a QRS portion of an ECGsignal. Next, if the width of the current peak is within the acceptablerange and if the height of the peak is greater than the adaptivethreshold, then that peak is considered a candidate to be an acceptablepeak for further processing. Peaks not meeting these requirements areignored. Next, for candidate acceptable peaks within a predeterminedtimeframe of one another, preferably 3/16 of a second of one another,the heights of the peaks are compared to one another and the lower peaksin that time frame are ignored. If there is only one candidateacceptable peak within the timeframe, then that peak is considered acandidate acceptable peak. At this point, a number of candidateacceptable peaks will have been identified. Next, for each identifiedcandidate acceptable peak, the area between that peak and the last,being that immediately previous in time, candidate acceptable peak isexamined for any other signal peaks having a height that is greater than0.75 of the height of the current candidate acceptable peak. If thereare more than a predetermined number, preferably 2, such peaksidentified, then the current candidate acceptable peak is invalidatedand ignored for further processing. In addition, if there are any hitsof the rail as described above between the last candidate acceptablepeak and the current candidate acceptable peak, then the currentcandidate acceptable peak is invalidated and ignored for furtherprocessing. When these steps are completed, a number of acceptable peakswill have been identified in the signal, each one being deemed anacceptable heart beat spike that may be used to calculate heart relatedparameters therefrom, including, but not limited to, heart rate.

According to an alternate embodiment for identifying acceptable heartbeat spikes, each up-down-up sequence, a possible QRST sequence, in thenon-noise signal is first identified. As used herein, an up-down-upsequence refers to a sequence on the non-noise signal having anincreasing amplitude portion followed by a maximum amplitude portionfollowed by a decreasing amplitude portion followed by a minimumamplitude portion followed by an increasing amplitude portion. Anexample of such up-down-up sequence is shown in FIG. 10 and includespoints A, B, C, and D wherein the X axis is time and the Y axis issignal strength or amplitude. After each up-down-up sequence isidentified, the height, in terms of amplitude, and the width, in termsof time, of each up-down-up sequence is calculated. Preferably, theheight for each up-down-up sequence is determined as follows:(BY−AY)+(BY−CY)+(DY−CY), and the width for each peak is determined asfollows: (DX−AX).

Next, the height of each up-down-up sequence is compared to apredetermined threshold value, preferably an adaptive threshold such assome percentage, e.g., 75%, of the moving average of previous heights,and the width of each up-down-up sequence is compared to a predeterminedthreshold value range, preferably equal to 4 to 20 points when a 128 Hzanalog to digital sampling rate is used, which represents a typicalrange of widths of a QRST sequence of an ECG signal. If the height isgreater than the threshold and the width is within than thepredetermined threshold value range, then that up-down-up sequence isconsidered to be a candidate acceptable QRST sequence. Next, for eachidentified candidate acceptable QRST sequence in the non-noise signal, asurrounding time period window having a predetermined length, preferably3/16 of a second, is examined and the height of the current candidateacceptable QRST sequence in the time period window is compared to allother identified candidate acceptable QRST sequences in the time periodwindow. The candidate acceptable QRST sequence having the largest heightin the time period window, which may or may not be the current candidateacceptable QRST sequence, is validated, and the other candidateacceptable QRST sequences in the time period window, which may includethe current candidate acceptable QRST sequence, are invalidated andignored for further processing. Once this step has been completed, anumber of acceptable QRST sequences will have been identified in thenon-noise signal. Next, for each acceptable QRST sequence that has beenidentified, the distance, in terms of time, to the immediately previousin time acceptable QRST sequence and the immediately next in time QRSTsequence are measured. Each distance is preferably measured from the Rpoint of one sequence to R point of the other sequence. The R point ineach acceptable QRST sequence corresponds to the point B shown in FIG.10, the highest amplitude point. In addition, two standard deviationsare calculated for each acceptable QRST sequence. The first standarddeviation is the standard deviation of the amplitude of all of thesampled points between the T point, which corresponds to point D shownin FIG. 10, of the current acceptable QRST sequence and the Q point,which corresponds to point A shown in FIG. 10, of the immediately nextin time acceptable QRST sequence. The other standard deviation is thestandard deviation of the amplitude of all of the sampled points betweenthe Q point, which corresponds to point A shown in FIG. 10, of thecurrent acceptable QRST sequence to the T point, which corresponds topoint D shown in FIG. 10, of the immediately previous in time QRSTsequence. Next, the two measured distances, the two standard deviationsand the calculated height and width of each acceptable QRST sequence areinput into a simple heart beat classifier, which decides whether theacceptable QRST sequence and the surrounding area is a qualifying heartbeat or is too noisy. For example, the heart beat classifier may be adecision tree that has been trained using previously obtained andlabeled heart beat data. Alternatively, the heart beat classifier may beany known classifier mechanism, including, but not limited to, decisiontrees, artificial neural networks, support vector machines, Bayesianbelief networks, naïve Bayes and decision lists.

Those sequences that are determined to be too noisy are ignored. Thus,upon completion of this step, a set of acceptable QRST sequences willhave been identified, the QRS, which corresponds to points A, B and C inFIG. 9, portion of each being deemed an acceptable heart beat spike thatmay be used to calculate various heart related parameters therefrom,including, but not limited to, heart rate.

According to an alternate embodiment for identifying acceptable heartbeat spikes, each up-down-up sequence, a possible QRST sequence, in thefiltered signal is first identified. The heights of the components ofthe sequence are then calculated. The allowed amplitude of the candidateQRST complexes are required to be at least double the estimatedamplitude of signal noise. In addition, the width of the sequence mustnot exceed 200 milliseconds, an upper limit for believable QRSTcomplexes. Next, if a candidate QRS complex is still viable, theplausibility of the location in time for the complex given the currentheart rate estimate is checked. If the change in heart rate implied bythe candidate beat is less than fifty percent then the sequence isidentified to be a heart beat. FIG. 7F shows this process utilizingdetected signal 75, plotted as a series of interconnected data pointsforming QRST complexes in box 81. Signal boundary boxes 83 identify thetwo QRST complexes in detected signal 75 which are eliminated becausethey fail the 50% test described above. Heart beat peak points 84 areillustrated in box 82 which represent the QRST complexes identified asbeats from box 81. Note the absence of heart beat peak points at thecorresponding locations. Additionally, respiration data, includingrespiration rate, can be extracted from ECG waveforms. Respirationresults in regular and detectable amplitude variations in the observedECG. In terms of the equivalent dipole model of cardiac electricalactivity, respiration induces an apparent modulation in the direction ofthe mean cardiac electrical axis.

Additional methodologies are presented for the analysis and display ofthe heart rate data. In each of these methods, the signal is seriallysegmented into a set of overlapping time slices based on identified QRSTsequences. Each time slice is preferably exactly centered on the R pointof a sequence and contains a fixed window of time, e.g. 1.5 seconds, oneither side of the R point of that sequence. Each time slice may containmore than one QRST sequence, but will contain at least one in the centerof the time slice. While the analysis is performed mathematically, agraphical description will provide the clearest understanding to thoseskilled in the art. Next, for a given point in time, some number of timeslices before and after a given time slice are merged together oroverlaid on the same graph. In one particular embodiment, 10 time slicesbefore and after a given point are overlaid on the same graph In termsof graphic display, which is how this data may be presented to the userin the form of output, the time slice segments are overlapped, wherebysome number of QRST sequences, or time slice segments, are overlaid onthe same graph. Each detected primary QRST sequence and the neighboringsequences within the time slice segment, preferably 1.5 seconds, areoverlaid on top of the other beats in that window. For example, in FIG.10A, a series of signals 50 are overlapped with each other with primarybeat 55 aligned between the overlapped signals. This is referred to asan AND-based overlapping-beat-graph. The average 60 of all thesuperimposed beats is also calculated and displayed. At the center ofthe graph, where primary beats 55 are aligned, the beats look verysimilar, and a clear signal is discernable. Also note that theneighboring beats 65 are tightly clustered, with some deviation, whichis an indicator of beat to beat variability. One skilled in the art willdiscern that the heart rate for this set of beats is easily extractedfrom such a graph by looking at the distance between the center QRScomplex and the center of the neighboring complexes. When the signal isvery clear, as in this example, the utility of this calculation islimited. However, when the signal is noisy and many false beats aredetected, this technique can allow for finding a heart rate when thesignal itself is too noisy to use simplistic or observational methods.

Another embodiment of the overlapping-beat-graph involves using aADD-based approach to overlaps. In this version, as illustrated in FIG.10B, when the beats and the neighboring signal overlap, the intensity ofthe pixel in the resulting graph is increased by the number ofoverlapping points. FIG. 10B illustrates an example for the ECG signalshown where the base color is black and each signal that overlaps makesthe color brighter. Again, primary beat 55 is utilized to align the timeslice segments and the neighboring beats 65 are shown as more of a cloudof points than in FIG. 10A. The width of this cloud of points is relatedto the beat to beat variability of the signal in question. Even thoughindividual beats may not be reliably detected and the overlapped graphmay not show a clear pattern in the lines, the average 60, as shown inFIG. 10A may be utilized to identify clear neighboring QRS complexes.From these, a rate can be determined from the distance from the centerof the time slice to the center of the cloud of points representing theneighboring QRS sequences. An ADD-graph may be utilized to identifydistinct spikes for the neighboring QRS complexes in the presence ofsignificant noise to enhance the capabilities of the system. In analternate embodiment, the display could be biased more heavily towardthose pixels with more overlapping points such that if the number ofoverlapping points is X at a particular pixel, its intensity couldrepresented as X1.5, thereby more selectively highlighting the mostoverlapped points.

A method of establishing a database or other reference for themorphologies of the user's heart beat signal would necessarily includethe ability to classify heart beat patterns and to identify certainmorphologies. These patterns and morphologies could then be associatedwith certain activities or conditions. The first step, however, is toidentify the morphologies and patterns, as follows.

For example, a set of N ECG wave forms may be selected. The averagedistance between beats is identified and a time period ½ of theinterbeat period before and ½ of the interbeat period after to truncateeach waveform. It is specifically noted that other clipping distancesare possible and could be variable. As with the descriptions of beatmatching above, a graphic description of the process is the mostilluminating. N signal wave forms are detected in the clipping mode andare modeled, as with the ADD graphs above, with the signal featuresbeing measured by the intensity or brightness. The signal is assigned anintensity or numerical value. The surrounding area has no value. Theequator line of each wave form is identified, being that horizontal linesuch that the areas above and below this line are equal. A meridian lineis identified for each wave peak as that vertical line that subdividesthe QRS spike into two pieces, split at the peak value of the signal.All N images are overlapped such that all equators are coincident andall meridians are coincident. All intensity or numerical values for eachpoint in the N signals are normalized such that all values are betweentwo known boundary values, such as 0 and 1000. The result is arepresentation that captures the average heart beat morphology for thatperson over that period of time including, within the non-coincidentareas, signal segments where the wave forms tend to be most coincident,having the highest values and the least coincident, having the lowestvalues. In addition, each of the N images could be scaled prior tooverlap, wherein the height of the R point of each wave forms aconstant. Additionally, accuracy may be increased by selecting Xsegments of X wave forms in row and performing the above analysis withthe sequence of X wave forms instead of just with one.

As will be appreciated by those of skill in the art, it is possible thatthe signal output by analog to digital converter 160 may have itspolarity inverted as compared to what is expected from an ECG signal dueto the placement of electrodes 150, in which case what would otherwisebe peaks in the signal will appear as valleys in the signal. In such acase, the processing described above may be successfully performed onthe signal by first inverting its polarity. In one embodiment of thepresent invention, the signal output by analog to digital converter 160may be processed twice as described above, first without inverting itspolarity and then again after its polarity has been inverted, with thebest output being used for further processing as described herein.Additionally, the use of multiple sensors, such as an accelerometer oralternative pairs of electrodes, can be utilized to direct variable gainand dynamic signal thresholds or conditions during the signal processingin order to better adjust the types or nature of the processing to beapplied. Additionally, a peak detector circuit may be employed such asthat manufactured by Salutron, Fremont, Calif.

In addition, the system may detect known and recognizable contexts orsignal patterns that will simply not present an acceptable signal thatis discernable by the algorithms for beat and other body potentialrelated feature detection. In these situations, the system simplyrecognizes this condition and records the data stream, such as when EMGor motion amplitude is at a peak level, the system detects thiscondition and discontinues attempting to process the signal until thenext appropriate signal is received, according to certain preset ordynamically calculated conditions or thresholds. In some cases, theoutput of other sensors may be utilized to confirm the presence of acondition, such as excessive body motion, which would confirm that thesystem is operating properly, but lacking a coherent signal, as well asprovide a basis for interpolation of the data from the missing segmentof time. Under these conditions, a returned value from the system thatno heart information could reliably collected is itself of value,relative to returning erroneous heart information.

Once acceptable heart beat spikes have been identified from the signalthat is output by analog to digital converter 160 using one of themethods described herein, the acceptable heart beat spikes may be usedto calculate heart rate using any of several methods. While merelycounting the number of acceptable heart beat spikes in a particular timeperiod, such as a minute, might seem like an acceptable way to calculateheart rate, it will be appreciated that such a method will actuallyunderestimate heart rate because of the fact that a number of beats willlikely have been invalidated as noise as described above. Thus, heartrate and other heart related parameters such as beat to beat variabilityand respiration rate must be calculated in a manner that accounts forinvalidated beats. According to one embodiment, heart rate may becalculated from identified acceptable heart beat spikes by determiningthe distance, in time, between each group of two successive acceptableheart beat spikes identified in the signal and dividing sixty seconds bythis time to get a local heart rate for each group of two successiveacceptable heart beat spikes. Then, an average, median and/or peak ofall of such local heart rates may be calculated in a given time periodand used as the calculated heart rate value.

In the event that a period of time is encountered where no signal isavailable of a minimum level of quality for beat detection, amethodology must be developed by which the events of this time periodare estimated. The system provides the ability to produce accuratestatements about some heart parameters, including heart rate, for thismissing time period. A probability is assigned to the heart beatfrequency based upon the prior data which is reliable, by takingadvantage of previously learned data and probabilities about how heartrates change through time. This is not limited to the time periodimmediately prior to the missing time segment, although this may be thebest indicator of the missing section. The comparison can also be madeto prior segments of time which have been stored and or categorized, orthrough matching to a database of information relating to heartparameters under certain conditions. The system can also take advantageof other sensors utilized in conjunction with the device in thesecomputations of probability. For example the probability of missingheart beats on the heart beat channels can be utilized given that thevariance of the accelerometer sensor is high. This enables very accurateassessments of different rate sequences and allows the calculation of alikely heart rate. This method is most successful when some minimumnumber of detected beats are present.

An additional method of estimating activity during missing time periodsis to first identify candidate beats using one of the methods discussedabove. Any detection technique that also produces a strength value canbe used. In the preferred embodiment the detector will associate aprobability that the located beat is in fact a heart beat. Binarytrue/false detectors can be used by using as strength value 1 for truth.Next, all pairs of potential beats are combined to give a set ofinter-beat gaps. Each inter-beat gap defines a weighting function whosevalues are based on a combination of the size of the gap, the amount oftime which has passed since the gap was detected, the strength of theidentification and any meta-parameters needed by the family of weightingfunctions. In the preferred embodiment this weighting function is theinverse notch function. The inter-beat gap, in units of seconds,determines the location of the notch's peak. The height of the notch isdriven by the strength of the identification, the length of time sincethe gap was identified, as age, and a hyper-parameter referred to aslifetime. The width of the notch is defined by the hyper-parameterwidth. FIG. 7G shows this inverse notch function including notch peak 87and notch width 89. The function itself is mathematically expressed as:

In the third step, the individual weighting functions are summed toobtain a total weighting function. Finally, the resulting function isprogrammatically analyzed to obtain an estimate of heart rate.

In the preferred embodiment, the estimate of the true inter-beat gap istaken to be the value at which the function reaches its first localmaximum. FIG. 7H shows the resulting function and indicates the firstlocal maximum 91. Once the inter-beat gap is selected, the heart rate isdetermined from the formula HeartRate=60/InterbeatGap.

To minimize the processing load associated with the evaluation of thetotal weighting function, those individual weighting functions whoseinter-beat gaps are either larger or smaller than is physiologicallypossible are eliminated. In addition, individual functions whose age hasexceeded the value of the lifetime hyper parameter are also eliminated.

Another embodiment utilizes probabilistic filters on the allowedinter-beat gaps instead of a hard truncation as described above. Theseprobabilistic filters take as input one or more signals in addition tothe ECG signal and determine a probabilistic range for the allowableheart beat. One instantiation of this is to determine the context of thewearer from the non-ECG signals and then, for each context, to apply aparticular Gaussian distribution with parameters determined by thecontext, the wearer's body parameters, as well as the ECG signal itself.Other probability distributions can easily be utilized as well for thisbiasing. This probability can then be multiplied by the probability ofeach inter-beat gap to produce a posterior distribution, from which themost likely heart beat can be easily determined.

Another aspect of the present invention is that during times whencertain heart parameters are not computable due to noise, theseparameters can also be estimated from the set of measured values nearbyin time and the sequences of other measurements made on other sensors.One such embodiment of this method is a contextual predictor similar tothat used for energy expenditure, but instead used to predict heart ratefrom accelerometer data, galvanic skin response data, skin temperatureand cover temperature data, as well as steps taken and other derivedphysiological and contextual parameters. This method first identifiesthe wearer's activity, and then applies an appropriate derivation forthat activity. In the preferred embodiment, all derivations for allactivities are applied and combined according to the probability of thatactivity being performed.

An additional aspect of the invention is a method of adaptation overtime for a particular user through the use of multiple noisy signalsthat provide feedback as to the quality of other derived signals.Another way of viewing this is as a method of calibration for a givenuser. First, a given derived parameter is calculated, representing somephysiological state of the wearer. Second, a second derived parameter iscalculated, representing the same physiological state. These two derivedparameters are compared, and used to adjust one another, according tothe confidences calculated for each of the derived metrics. Thecalculations are designed to accept a feedback signal to allow fortraining or tuning them. In one embodiment, this consists of merelyutilizing gradient descent to tune the parameters based on theadmittedly noisy feedback signal. In another embodiment, this involvesupdating a set of constants utilized in the computation based on asystem of probabilistic inference.

According to one aspect of the present invention, an algorithmdevelopment process is used to create a wide range of algorithms forgenerating continuous information relating to a variety of variablesfrom the data received from the plurality of physiological and/orcontextual sensors on armband body monitoring device 300, as identifiedin Table I hereto, including the ECG signal generated using electrodes105 that is used to calculate heart rate and other heart relatedparameters, many of which cannot be distinguished by visual recognitionfrom graphical data output and diagnostics alone. These include heartrate variability, heart rate deviation, average heart rate, respirationrate, atrial fibrillation, arrhythmia, inter-beat intervals, inter-beatinterval variability and the like. Additionally, continuous monitoringof this type, coupled with the ability to event- or time-stamp the datain real time, provides the ability to titrate the application of drugsor other therapies and observe the immediate and long term effectsthereof. Moreover, the ability is presented, through pattern recognitionand analysis of the data output, to predict certain conditions, such ascardiac arrhythmias, based upon prior events. Such variables mayinclude, without limitation, energy expenditure, including resting,active and total values; daily caloric intake; sleep states, includingin bed, sleep onset, sleep interruptions, wake, and out of bed; andactivity states, including exercising, sitting, traveling in a motorvehicle, and lying down. The algorithms for generating values for suchvariables may be based on data from, for example, an axis or both axesof a 2-axis accelerometer, a heat flux sensor, a GSR sensor, a skintemperature sensor, a near-body ambient temperature sensor, and a heartrate sensor in the embodiments described herein. Additionally, throughthe pattern detection and prediction capabilities described above, thesystem may predict the onset of certain events such as syncope,arrhythmia and certain physiological mental health states byestablishing a known condition set of parameters during one such episodeof such an event and detecting similar pre-event parameters. An alarm orother feedback would be presented to the user upon the reoccurrence ofthat particular set of parameters matching the prior event.

The monitoring device is capable of generating data indicative ofvarious additional physiological parameters of an individual which wouldbe helpful as part of the predictive and parameter identificationfunctionality described above. This includes, in addition to thoseparameters described elsewhere, respiration rate, skin temperature, corebody temperature, heat flow off the body, galvanic skin response or GSR,EMG, EEG, EOG, blood pressure, body fat, hydration level, activitylevel, oxygen consumption, glucose or blood sugar level, body position,pressure on muscles or bones, and UV radiation exposure and absorption.In certain cases, the data indicative of the various physiologicalparameters is the signal or signals themselves generated by the one ormore sensors and in certain other cases the data is calculated by themicroprocessor based on the signal or signals generated by the one ormore sensors. Methods for generating data indicative of variousphysiological parameters and sensors to be used therefor are well known.Table 1 provides several examples of such well known methods and showsthe parameter in question, the method used, the sensor device used, andthe signal that is generated. Table 1 also provides an indication as towhether further processing based on the generated signal is required togenerate the data.

TABLE 1 Further Parameter Example Method Example Sensor SignalProcessing Heart Rate EKG 2 Electrodes DC Voltage Yes Pulse Rate BVP LEDEmitter and Change in Resistance Yes Optical Sensor Beat-to-Beat HeartRate 2 Electrodes DC Voltage Yes Variability EKG Skin Surface 3-10Electrodes DC Voltage No Potentials Respiration Rate Chest Volume StrainGauge Change in Resistance Yes Change Skin Temperature SurfaceThermistors Change in Resistance Yes Temperature Probe Core TemperatureEsophageal or Thermistors Change in Resistance Yes Rectal Probe HeatFlow Heat Flux Thermopile DC Voltage Yes Galvanic Skin Skin Conductance2 Electrodes Change in Resistance No Response EMG Skin Surface 3Electrodes DC Voltage No Potentials EEG Skin Surface Multiple ElectrodesDC Voltage Yes Potentials EOG Eye Movement Thin Film DC Voltage YesPiezoelectric Sensors Blood Pressure Non-Invasive Electronic Change inResistance Yes Korotkuff Sounds Sphygromarometer Body Fat Body Impedance2 Active Electrodes Change in Impedance Yes Activity in Body MovementAccelerometer DC Voltage, Yes Interpreted G Capacitance Changes Shocksper Minute Oxygen Oxygen Uptake Electro-chemical DC Voltage Change YesConsumption Glucose Level Non-Invasive Electro-chemical DC VoltageChange Yes Body Position (e.g. N/A Mercury Switch DC Voltage Change Yessupine, erect, Array sitting) Muscle Pressure N/A Thin Film DC VoltageChange Yes Piezoelectric Sensors UV Radiation N/A UV Sensitive Photo DCVoltage Change Yes Absorption Cells

It is to be specifically noted that a number of other types andcategories of sensors may be utilized alone or in conjunction with thosegiven above, including but not limited to relative and globalpositioning sensors for determination of motion or location of the user;torque & rotational acceleration for determination of orientation inspace; blood chemistry sensors; interstitial fluid chemistry sensors;bio-impedance sensors; and several contextual sensors, such as: pollen,humidity, ozone, acoustic, body and ambient noise and sensors adapted toutilize the device in a biofingerprinting scheme.

The types of data listed in Table 1 are intended to be examples of thetypes of data that can be generated by the monitoring device. It is tobe understood that other types of data relating to other parameters canbe generated without departing from the scope of the present invention.Additionally, certain information may be derived from the above data,relating to an individual's physiological state. Table 2 providesexamples of the type of information that can be derived, and indicatessome of the types of data that can be used therefor.

TABLE 2 Derived Information Example of Data Used Ovulation Skintemperature, core temperature, oxygen consumption Sleep onset/wakeBeat-to-beat variability, heart rate, pulse rate, respiration rate, skintemperature, core temperature, heat flow, galvanic skin response, EMG,EEG, EOG, blood pressure, oxygen consumption Calories burned Heart rate,pulse rate, respiration rate, heat flow, activity, oxygen consumptionBasal metabolic rate Heart rate, pulse rate, respiration rate, heatflow, activity, oxygen consumption Basal temperature Skin temperature,core temperature Activity level Heart rate, pulse rate, respirationrate, heat flow, activity, oxygen consumption Stress level EKG,beat-to-beat variability, heart rate, pulse rate, respiration rate, skintemperature, heat flow, galvanic skin response, EMG, EEG, bloodpressure, activity, oxygen consumption Relaxation level EKG,beat-to-beat variability, heart rate, pulse rate, respiration rate, skintemperature, heat flow, galvanic skin response, EMG, EEG, bloodpressure, activity, oxygen consumption Maximum oxygen consumption rateEKG, heart rate, pulse rate, respiration rate, heat flow, bloodpressure, activity, oxygen consumption Rise time or the time it takes torise Heart rate, pulse rate, heat flow, oxygen consumption from aresting rate to 85% of a target maximum Time in zone or the time heartrate was Heart rate, pulse rate, heat flow, oxygen consumption above 85%of a target maximum Recovery time or the time it takes heart Heart rate,pulse rate, heat flow, oxygen consumption rate to return to a restingrate after heart rate was above 85% of a target maximum

Additionally, the device may also generate data indicative of variouscontextual parameters such as activity states or other data relating tothe environment surrounding the individual. For example, air quality,sound level/quality, light quality or ambient temperature near theindividual, or even the global positioning of the individual.

In order to derive information from the sensors and data types herein, aseries of algorithms are developed for predicting user characteristics,continual measurements, durative contexts, instantaneous events, andcumulative conditions. User characteristics include permanent andsemi-permanent parameters of the wearer, including aspects such asweight, height, and wearer identity. An example of a continualmeasurement is energy expenditure, which constantly measures, forexample on a minute by minute basis, the number of calories of energyexpended by the wearer. Durative contexts are behaviors that last someperiod of time, such as sleeping, driving a car, or jogging.Instantaneous events are those that occur at a fixed or over a veryshort time period, such as a heart attack or falling down. Cumulativeconditions are those where the person's condition can be deduced fromtheir behavior over some previous period of time. For example, if aperson hasn't slept in 36 hours and hasn't eaten in 10 hours, it islikely that they are fatigued. Table 3 below shows numerous examples ofspecific personal characteristics, continual measurements, durativemeasurements, instantaneous events, and cumulative conditions.

TABLE 3 Example personal age, sex, weight, gender, athletic ability,characteristics conditioning, disease, height, susceptibility todisease, activity level, individual detection, handedness, metabolicrate, body composition Example continual mood, beat-to-beat variabilityof heart beats, measurements respiration, energy expenditure, bloodglucose levels, level of ketosis, heart rate, stress levels, fatiguelevels, alertness levels, blood pressure, readiness, strength,endurance, amenability to interaction, steps per time period, stillnesslevel, body position and orientation, cleanliness, mood or affect,approachability, caloric intake, TEF, XEF, 'in the zone'-ness, activeenergy expenditure, carbohydrate intake, fat intake, protein intake,hydration levels, truthfulness, sleep quality, sleep state,consciousness level, effects of medication, dosage prediction, waterintake, alcohol intake, dizziness, pain, comfort, remaining processingpower for new stimuli, proper use of the armband, interest in a topic,relative exertion, location, blood-alcohol level Example durativeexercise, sleep, lying down, sitting, standing, measurements ambulation,running, walking, biking, stationary biking, road biking, liftingweights, aerobic exercise, anaerobic exercise, strength- buildingexercise, mind-centering activity, periods of intense emotion, relaxing,watching TV, sedentary, REM detector, eating, in-the- zone,interruptible, general activity detection, sleep stage, heat stress,heat stroke, amenable to teaching/learning, bipolar decompensation,abnormal events (in heart signal, in activity level, measured by theuser, etc), startle level, highway driving or riding in a car, airplanetravel, helicopter travel, boredom events, sport detection (football,baseball, soccer, etc), studying, reading, intoxication, effect of adrug Example instantaneous falling, heart attack, seizure, sleep arousalevents events, PVCs, blood sugar abnormality, acute stress ordisorientation, emergency, heart arrhythmia, shock, vomiting, rapidblood loss, taking medication, swallowing Example cumulativeAlzheimer's, weakness or increased likelihood conditions of falling,drowsiness, fatigue, existence of ketosis, ovulation, pregnancy,disease, illness, fever, edema, anemia, having the flu, hypertension,mental disorders, acute dehydration, hypothermia, being-in-the-zone

It will be appreciated that the present invention may be utilized in amethod for doing automatic journaling of a wearer's physiological andcontextual states. The system can automatically produce a journal ofwhat activities the user was engaged in, what events occurred, how theuser's physiological state changed over time, and when the userexperienced or was likely to experience certain conditions. For example,the system can produce a record of when the user exercised, drove a car,slept, was in danger of heat stress, or ate, in addition to recordingthe user's hydration level, energy expenditure level, sleep levels, andalertness levels throughout a day. These detected conditions can beutilized to time- or event-stamp the data record, to modify certainparameters of the analysis or presentation of the data, as well astrigger certain delayed or real time feedback events.

According to the algorithm development process, linear or non-linearmathematical models or algorithms are constructed that map the data fromthe plurality of sensors to a desired variable. The process consists ofseveral steps. First, data is collected by subjects wearing armband bodymonitoring device 300 who are put into situations as close to real worldsituations as possible, with respect to the parameters being measured,such that the subjects are not endangered and so that the variable thatthe proposed algorithm is to predict can, at the same time, be reliablymeasured using, for example, highly accurate medical grade labequipment. This first step provides the following two sets of data thatare then used as inputs to the algorithm development process: (i) theraw data from armband body monitoring device 300, and (ii) the dataconsisting of the verifiably accurate data measurements and extrapolatedor derived data made with or calculated from the more accurate labequipment. This verifiable data becomes a standard against which otheranalytical or measured data is compared. For cases in which the variablethat the proposed algorithm is to predict relates to context detection,such as traveling in a motor vehicle, the verifiable standard data isprovided by the subjects themselves, such as through information inputmanually into armband body monitoring device 300, a PC, or otherwisemanually recorded. The collected data, i.e., both the raw data and thecorresponding verifiable standard data, is then organized into adatabase and is split into training and test sets.

Next, using the data in the training set, a mathematical model is builtthat relates the raw data to the corresponding verifiable standard data.Specifically, a variety of machine learning techniques are used togenerate two types of algorithms: 1) algorithms known as features, whichare derived continuous parameters that vary in a manner that allows theprediction of the lab-measured parameter for some subset of the datapoints. The features are typically not conditionally independent of thelab-measured parameter e.g. VO2 level information from a metabolic cart,douglas bag, or doubly labeled water, and 2) algorithms known as contextdetectors that predict various contexts, e.g., running, exercising,lying down, sleeping or driving, useful for the overall algorithm. Anumber of well known machine learning techniques may be used in thisstep, including artificial neural nets, decision trees, memory-basedmethods, boosting, attribute selection through cross-validation, andstochastic search methods such as simulated annealing and evolutionarycomputation.

After a suitable set of features and context detectors are found,several well known machine learning methods are used to combine thefeatures and context detectors into an overall model. Techniques used inthis phase include, but are not limited to, multilinear regression,locally weighted regression, decision trees, artificial neural networks,stochastic search methods, support vector machines, and model trees.These models are evaluated using cross-validation to avoid over-fitting.

At this stage, the models make predictions on, for example, a minute byminute basis. Inter-minute effects are next taken into account bycreating an overall model that integrates the minute by minutepredictions. A well known or custom windowing and threshold optimizationtool may be used in this step to take advantage of the temporalcontinuity of the data. Finally, the model's performance can beevaluated on the test set, which has not yet been used in the creationof the algorithm. Performance of the model on the test set is thus agood estimate of the algorithm's expected performance on other unseendata. Finally, the algorithm may undergo live testing on new data forfurther validation.

Further examples of the types of non-linear functions and/or machinelearning method that may be used in the present invention include thefollowing: conditionals, case statements, logical processing,probabilistic or logical inference, neural network processing, kernelbased methods, memory-based lookup including kNN and SOMs, decisionlists, decision-tree prediction, support vector machine prediction,clustering, boosted methods, cascade-correlation, Boltzmann classifiers,regression trees, case-based reasoning, Gaussians, Bayes nets, dynamicBayesian networks, HMMs, Kalman filters, Gaussian processes andalgorithmic predictors, e.g. learned by evolutionary computation orother program synthesis tools.

Although one can view an algorithm as taking raw sensor values orsignals as input, performing computation, and then producing a desiredoutput, it is useful in one preferred embodiment to view the algorithmas a series of derivations that are applied to the raw sensor values.Each derivation produces a signal referred to as a derived channel. Theraw sensor values or signals are also referred to as channels,specifically raw channels rather than derived channels. Thesederivations, also referred to as functions, can be simple or complex butare applied in a predetermined order on the raw values and, possibly, onalready existing derived channels. The first derivation must, of course,only take as input raw sensor signals and other available baselineinformation such as manually entered data and demographic informationabout the subject, but subsequent derivations can take as inputpreviously derived channels. Note that one can easily determine, fromthe order of application of derivations, the particular channelsutilized to derive a given derived channel. Also note that inputs that auser provides on an Input/Output, or I/O, device or in some fashion canalso be included as raw signals which can be used by the algorithms. Forexample, the category chosen to describe a meal can be used by aderivation that computes the caloric estimate for the meal. In oneembodiment, the raw signals are first summarized into channels that aresufficient for later derivations and can be efficiently stored. Thesechannels include derivations such as summation, summation ofdifferences, and averages. Note that although summarizing the high-ratedata into compressed channels is useful both for compression and forstoring useful features, it may be useful to store some or all segmentsof high rate data as well, depending on the exact details of theapplication. In one embodiment, these summary channels are thencalibrated to take minor measurable differences in manufacturing intoaccount and to result in values in the appropriate scale and in thecorrect units. For example, if, during the manufacturing process, aparticular temperature sensor was determined to have a slight offset,this offset can be applied, resulting in a derived channel expressingtemperature in degrees Celsius.

For purposes of this description, a derivation or function is linear ifit is expressed as a weighted combination of its inputs together withsome offset. For example, if G and H are two raw or derived channels,then all derivations of the form A*G+B*H+C, where A, B, and C areconstants, is a linear derivation. A derivation is non-linear withrespect to its inputs if it can not be expressed as a weighted sum ofthe inputs with a constant offset. An example of a nonlinear derivationis as follows: if G>7 then return H*9, else return H*3.5+912. A channelis linearly derived if all derivations involved in computing it arelinear, and a channel is nonlinearly derived if any of the derivationsused in creating it are nonlinear. A channel nonlinearly mediates aderivation if changes in the value of the channel change the computationperformed in the derivation, keeping all other inputs to the derivationconstant.

According to a preferred embodiment of the present invention, thealgorithms that are developed using this process will have the formatshown conceptually in FIG. 11. Specifically, the algorithm will take asinputs the channels derived from the sensor data collected by armbandbody monitoring device 300 from the various sensors, including the heartrate and other heart related parameters calculated from the ECG signalgenerated using electrodes 105 and demographic information for theindividual as shown in box 400. The algorithm includes at least onecontext detector 405 that produces a weight, shown as W1 through WN,expressing the probability that a given portion of collected data, suchas is collected over a minute, was collected while the wearer was ineach of several possible contexts. Such contexts may include whether theindividual was at rest or active. In addition, for each context, aregression algorithm 410 is provided where a continuous prediction iscomputed taking raw or derived channels as input. The individualregressions can be any of a variety of regression equations or methods,including, for example, multivariate linear or polynomial regression,memory based methods, support vector machine regression, neuralnetworks, Gaussian processes, arbitrary procedural functions and thelike. Each regression is an estimate of the output of the parameter ofinterest in the algorithm, for example, energy expenditure. Finally, theoutputs of each regression algorithm 410 for each context, shown as A1through AN, and the weights W1 through WN are combined in apost-processor 415 which outputs the parameter of interest beingmeasured or predicted by the algorithm, shown in box 420. In general,the post-processor 415 can consist of any of many methods for combiningthe separate contextual predictions, including committee methods,boosting, voting methods, consistency checking, or context basedrecombination.

Referring to FIG. 12, an example algorithm for measuring energyexpenditure of an individual is shown. This example algorithm may be runon armband body monitoring device 300 having at least an accelerometer,a heat flux sensor and a GSR sensor, or an I/O device that receives datafrom such an armband body monitoring device as is disclosed inco-pending U.S. patent application Ser. No. 10/682,759, thespecification of which is incorporated herein by reference. In thisexample algorithm, the raw data from the sensors is calibrated andnumerous values based thereon, i.e., derived channels, are created. Inparticular, the following derived channels, shown at 400 in FIG. 12, arecomputed from the raw signals and the demographic information: (1)longitudinal accelerometer average, or LAVE, based on the accelerometerdata; (2) transverse accelerometer sum of average differences, or TSAD,based on the accelerometer data; (3) heat flux high gain averagevariance, or HFvar, based on heat flux sensor data; (4) vector sum oftransverse and longitudinal accelerometer sum of absolute differences orSADs, identified as VSAD, based on the accelerometer data; (5) galvanicskin response, or GSR, in both low and combined gain embodiments; and(6) Basal Metabolic Rate or BMR, based on demographic information inputby the user. Context detector 405 consists of a naïve Bayesianclassifier that predicts whether the wearer is active or resting usingthe LAVE, TSAD, and HFvar derived channels. The output is aprobabilistic weight, W1 and W2 for the two contexts rest and active.For the rest context, the regression algorithm 410 is a linearregression combining channels derived from the accelerometer, the heatflux sensor, the user's demographic data, and the galvanic skin responsesensor. The equation, obtained through the algorithm design process, isA*VSAD+B*HFvar+C*GSR+D*BMR+E, where A, B, C, D and E are constants. Theregression algorithm 410 for the active context is the same, except thatthe constants are different. The post-processor 415 for this example isto add together the weighted results of each contextual regression. IfA1 is the result of the rest regression and A2 is the result of theactive regression, then the combination is just W1*A1+W2*A2, which isenergy expenditure shown at 420. In another example, a derived channelthat calculates whether the wearer is motoring, that is, driving in acar at the time period in question might also be input into thepost-processor 415. The process by which this derived motoring channelis computed is algorithm 3. The post-processor 415 in this case mightthen enforce a constraint that when the wearer is predicted to bedriving by algorithm 3, the energy expenditure is limited for that timeperiod to a value equal to some factor, e.g. 1.3 times their minute byminute basal metabolic rate.

As another example, an algorithm having the format shown conceptually inFIG. 11 may be developed for measuring energy expenditure of anindividual that utilizes as inputs the channels derived from the sensordata collected by armband body monitoring device 300 from the 2-axisaccelerometer and the electrodes 105, from which heart rate and/or otherheart related parameters are calculated. The parameters derived fromthese motion and heart rate sensor types are largely orthogonal and arevery descriptive of a user's activities. The combination of these twosensors in an algorithm having the format shown conceptually in FIG. 11provides the ability to easily distinguish between different activityclasses that might be confusing to a single sensor, such as stressfulevents, some of which could be identified by high heart rate and lowmotion, vehicular motion events, some of which could be identified bylow heart rate and high motion and exercise events, some of which couldbe identified by high heart rate and high motion. As shown in FIG. 11,in this embodiment, the channels derived from the sensor data from thesetwo sensors are first used to detect the context of the user. Theappropriate function or functions are then used to predict energyexpenditure based on both heart rate and motion data. As a furtheralternative, channels derived from additional sensors forming a part ofarmband body monitoring device 300, such as a heat flux sensor may alsobe used as additional inputs into the algorithm. Using heart rate in analgorithm for predicting energy expenditure can result in a better, moreaccurate prediction for a number of reasons. For example, some lowmotion exercises such as biking or weight lifting pose issues for anenergy expenditure algorithm that uses arm motion from an accelerometeras a sole input. Also, clothing may adversely affect measurements madeby a heat flux sensor, which in turn may adversely effect energyexpenditure predictions. Incorporating heart rate or other heart relatedparameters into an algorithm helps to alleviate such problems.Obviously, there is considerable utility in the mere detection, analysisand reporting of the heart rate and other heart related parametersalone, other than for use in such algorithms. Moreover, heart rategenerally slows when someone falls asleep, and rises during REM periods.Thus, algorithms for predicting whether someone is sleeping and whatstage of sleep they are in may be developed in accordance with thepresent invention that utilize as an input, along with other sensordata, data collected by armband body monitoring device 300 from theelectrodes 105 from which heart rate and/or other heart relatedparameters are calculated as well as the other detected data typesidentified herein. Such heart related data may also be used inalgorithms for detecting various sleep disorders, such as sleep apnea.Similarly, when under stress, a person's heart rate often rises withoutan accompanying increase in motion or body heat. Day to day or timeperiod to time period comparisons of such data for an individual willassist in identifying certain patterns or conditions which may be usedfor both further pattern detection or prediction. Algorithms fordetecting stress may be developed in accordance with the presentinvention that utilize data collected from the electrodes 105, fromwhich heart rate and/or other heart related parameters are calculated,along with other sensor data such as data from an accelerometer. Whilethe applicability of recognizing stress is most likely in the context ofreviewing past activity and attempting to correlate the detected andderived parameters with life activities or other non-detectable events,the ability to detect stress may be effective as a contemporaneousmeasurement to identify a condition that may be masked from the wearerby external conditions or merely preoccupation. This is especially truein the event that the heart is undergoing stress in the absence ofphysical exertion or activity.

Other important feedback embodiments include the ability to detect REMsleep through the heart related parameters and to maximize the wearer'sopportunity to engage in such sleep. Rather than the conventional alarmwaking the user at a preappointed time, the alarm could wake the wearerafter a preset amount of REM sleep, and further at an appropriateendpoint of such sleep or during or just after some particular sleepstage.

This algorithm development process may also be used to create algorithmsto enable armband body monitoring device 300 to detect and measurevarious other parameters, including, without limitation, the following:(i) when an individual is suffering from duress, including states ofunconsciousness, fatigue, shock, drowsiness, heat stress anddehydration; and (ii) an individual's state of readiness, health and/ormetabolic status, such as in a military environment, including states ofdehydration, under-nourishment and lack of sleep. In addition,algorithms may be developed for other purposes, such as filtering,signal clean-up and noise cancellation for signals measured by a sensordevice as described herein. As will be appreciated, the actual algorithmor function that is developed using this method will be highly dependenton the specifics of the sensor device used, such as the specific sensorsand placement thereof and the overall structure and geometry of thesensor device. Thus, an algorithm developed with one sensor device willnot work as well, if at all, on sensor devices that are notsubstantially structurally identical to the sensor device used to createthe algorithm.

It is to be specifically understood that the method for creation ofalgorithms described above can be applied utilizing the detected signalfrom the apparatus as input to provide a methodology for beat detection.The detected signal is treated as a channel, as described above and thesame techniques are applied.

Another aspect of the present invention relates to the ability of thedeveloped algorithms to handle various kinds of uncertainty. Datauncertainty refers to sensor noise and possible sensor failures. Datauncertainty is when one cannot fully trust the data. Under suchconditions, for example, if a sensor, for example an accelerometer,fails, the system might conclude that the wearer is sleeping or restingor that no motion is taking place. Under such conditions it is very hardto conclude if the data is bad or if the model that is predicting andmaking the conclusion is wrong. When an application involves both modeland data uncertainties, it is very important to identify the relativemagnitudes of the uncertainties associated with data and the model. Anintelligent system would notice that the sensor seems to be producingerroneous data and would either switch to alternate algorithms or would,in some cases, be able to fill the gaps intelligently before making anypredictions. When neither of these recovery techniques are possible, aswas mentioned before, returning a clear statement that an accurate valuecan not be returned is often much preferable to returning informationfrom an algorithm that has been determined to be likely to be wrong.Determining when sensors have failed and when data channels are nolonger reliable is a non-trivial task because a failed sensor cansometimes result in readings that may seem consistent with some of theother sensors and the data can also fall within the normal operatingrange of the sensor.

Clinical uncertainty refers to the fact that different sensors mightindicate seemingly contradictory conclusions. Clinical uncertainty iswhen one cannot be sure of the conclusion that is drawn from the data.For example, the accelerometers might indicate that the wearer ismotionless, leading toward a conclusion of a resting user, the galvanicskin response sensor might provide a very high response, leading towarda conclusion of an active user, the heat flow sensor might indicate thatthe wearer is still dispersing substantial heat, leading toward aconclusion of an active user, and the heart rate sensor might indicatethat the wearer has an elevated heart rate, leading toward a conclusionof an active user. An inferior system might simply try to vote among thesensors or use similarly unfounded methods to integrate the variousreadings. The present invention weights the important jointprobabilities and determines the appropriate most likely conclusion,which might be, for this example, that the wearer is currentlyperforming or has recently performed a low motion activity such asstationary biking.

This same algorithm development process was used to develop thealgorithms disclosed above for detecting heart beats, for determiningheart rate, and for estimating heart rate in the presence of noise. Itwill be clear to one skilled in the art that this same process could beutilized to both incorporate other sensors to improve the measurement ofheart related parameters or to incorporate heart related parameters intothe measurement of other physiological parameters such as energyexpenditure.

According to a further aspect of the present invention, a sensor devicesuch as armband body monitoring device 300 may be used to automaticallymeasure, record, store and/or report a parameter Y relating to the stateof a person, preferably a state of the person that cannot be directlymeasured by the sensors. State parameter Y may be, for example andwithout limitation, calories consumed, energy expenditure, sleep states,hydration levels, ketosis levels, shock, insulin levels, physicalexhaustion and heat exhaustion, among others. The sensor device is ableto observe a vector of raw signals consisting of the outputs of certainof the one or more sensors, which may include all of such sensors or asubset of such sensors. As described above, certain signals, referred toas channels same potential terminology problem here as well, may bederived from the vector of raw sensor signals as well. A vector X ofcertain of these raw and/or derived channels, referred to herein as theraw and derived channels X, will change in some systematic way dependingon or sensitive to the state, event and/or level of either the stateparameter Y that is of interest or some indicator of Y, referred to asU, wherein there is a relationship between Y and U such that Y can beobtained from U. According to the present invention, a first algorithmor function f1 is created using the sensor device that takes as inputsthe raw and derived channels X and gives an output that predicts and isconditionally dependent, expressed with the symbol

, on (i) either the state parameter Y or the indicator U, and (ii) someother state parameter(s) Z of the individual. This algorithm or functionf1 may be expressed as follows:

f1(X)

U+Z

or

f1(X)

Y+Z

According to the preferred embodiment, f1 is developed using thealgorithm development process described elsewhere herein which usesdata, specifically the raw and derived channels X, derived from thesignals collected by the sensor device, the verifiable standard datarelating to U or Y and Z contemporaneously measured using a method takento be the correct answer, for example highly accurate medical grade labequipment, and various machine learning techniques to generate thealgorithms from the collected data. The algorithm or function f1 iscreated under conditions where the indicator U or state parameter Y,whichever the case may be, is present. As will be appreciated, theactual algorithm or function that is developed using this method will behighly dependent on the specifics of the sensor device used, such as thespecific sensors and placement thereof and the overall structure andgeometry of the sensor device. Thus, an algorithm developed with onesensor device will not work as well, if at all, on sensor devices thatare not substantially structurally identical to the sensor device usedto create the algorithm or at least can be translated from device todevice or sensor to sensor with known conversion parameters.

Next, a second algorithm or function f2 is created using the sensordevice that takes as inputs the raw and derived channels X and gives anoutput that predicts and is conditionally dependent on everything outputby f1 except either Y or U, whichever the case may be, and isconditionally independent, indicated by the symbol

, of either Y or U, whichever the case may be. The idea is that certainof the raw and derived channels X from the one or more sensors make itpossible to explain away or filter out changes in the raw and derivedchannels X coming from non-Y or non-U related events. This algorithm orfunction f2 may be expressed as follows:

f2(X)

Z and (f2(X)

Y or f2(X)

U

Preferably, f2, like f1, is developed using the algorithm developmentprocess referenced above. f2, however, is developed and validated underconditions where U or Y, whichever the case may, is not present. Thus,the gold standard data used to create f2 is data relating to Z onlymeasured using highly accurate medical grade lab equipment.

Thus, according to this aspect of the invention, two functions will havebeen created, one of which, f1, is sensitive to U or Y, the other ofwhich, f2, is insensitive to U or Y. As will be appreciated, there is arelationship between f1 and f2 that will yield either U or Y, whicheverthe case may be. In other words, there is a function f3 such that f3(f1, f2)=U or f3 (f1, f2)=Y. For example, U or Y may be obtained bysubtracting the data produced by the two functions (U=f1−f2 or Y=f1−f2).In the case where U, rather than Y, is determined from the relationshipbetween f1 and f2, the next step involves obtaining Y from U based onthe relationship between Y and U. For example, Y may be some fixedpercentage of U such that Y can be obtained by dividing U by somefactor.

One skilled in the art will appreciate that in the present invention,more than two such functions, e.g. (f1, f2, f3, . . . f_n−1) could becombined by a last function f_n in the manner described above. Ingeneral, this aspect of the invention requires that a set of functionsis combined whose outputs vary from one another in a way that isindicative of the parameter of interest. It will also be appreciatedthat conditional dependence or independence as used here will be definedto be approximate rather than precise. For example, it is known thattotal body metabolism is measured as total energy expenditure, or TEE,according to the following equation:

TEE=BMR+AE+TEF+AT,

wherein BMR is basal metabolic rate, which is the energy expended by thebody during rest such as sleep, AE is activity energy expenditure, whichis the energy expended during physical activity, TEF is thermic effectof food, which is the energy expended while digesting and processing thefood that is eaten, and AT is adaptive thermogenesis, which is amechanism by which the body modifies its metabolism to extremetemperatures. It is estimated that it costs humans about 10% of thevalue of food that is eaten to process the food. TEF is thereforeestimated to be 10% of the total calories consumed. Thus, a reliable andpractical method of measuring TEF would enable caloric consumption to bemeasured without the need to manually track or record food relatedinformation. Specifically, once TEF is measured, caloric consumption canbe accurately estimated by dividing TEF by 0.1 (TEF=0.1*CaloriesConsumed; Calories Consumed=TEF/0.1).

According to a specific embodiment of the present invention relating tothe automatic measurement of a state parameter Y as described above, asensor device as described above may be used to automatically measureand/or record calories consumed by an individual. In this embodiment,the state parameter Y is calories consumed by the individual and theindicator U is TEF. First, the sensor device is used to create f1, whichis an algorithm for predicting TEE. f1 is developed and validated onsubjects who ate food, in other words, subjects who were performingactivity and who were experiencing a TEF effect. As such, f1 is referredto as EE(gorge) to represent that it predicts energy expenditureincluding eating effects. The verifiable standard data used to create f1is a VO2 machine. The function f1, which predicts TEE, is conditionallydependent on and predicts the item U of interest, which is TEF. Inaddition, f1 is conditionally dependent on and predicts Z which, in thiscase, is BMR+AE+AT. Next, the sensor device is used to create f2, whichis an algorithm for predicting all aspects of TEE except for TEF. f2 isdeveloped and validated on subjects who fasted for a period of timeprior to the collection of data, preferably 4-6 hours, to ensure thatTEF was not present and was not a factor. Such subjects will beperforming physical activity without any TEF effect. As a result, f2 isconditionally dependent to and predicts BMR+AE+AT but is conditionallyindependent of and does not predict TEF. As such, f2 is referred to asEE(fast) to represent that it predicts energy expenditure not includingeating effects. Thus, f1 so developed will be sensitive to TEF and f2 sodeveloped will be insensitive to TEF. As will be appreciated, in thisembodiment, the relationship between f1 and f2 that will yield theindicator U, which in this case is TEF, is subtraction. In other words,EE (gorge)−EE (fast)=TEF.

In the most preferred embodiment, armband body monitoring device 300includes and/or is in communication with a body motion sensor such as anaccelerometer adapted to generate data indicative of motion, a skinconductance sensor such as a GSR sensor adapted to generate dataindicative of the resistance of the individual's skin to electricalcurrent, a heat flux sensor adapted to generate data indicative of heatflow off the body, a electrodes for generating an ECG signal from whichdata indicative of the rate or other characteristics of the heart beatsof the individual may be generated, and a temperature sensor adapted togenerate data indicative of a temperature of the individual's skin. Inthis preferred embodiment, these signals, in addition the demographicinformation about the wearer, make up the vector of signals from whichthe raw and derived channels X are derived. Most preferably, this vectorof signals includes data indicative of motion, resistance of theindividual's skin to electrical current, heat flow off the body, andheart rate.

Another specific instantiation where the present invention can beutilized relates to detecting when a person is fatigued. Such detectioncan either be performed in at least two ways. A first way involvesaccurately measuring parameters such as their caloric intake, hydrationlevels, sleep, stress, and energy expenditure levels using a sensordevice and using the two function (f1 and f2) approach to provide anestimate of fatigue. A second way involves directly attempting to modelfatigue using the direct derivational approach described in connectionwith FIGS. 11 and 12. The first way illustrates that complex algorithmsthat predict the wearer's physiologic state can themselves be used asinputs to other more complex algorithms. One potential application forsuch an embodiment of the present invention would be forfirst-responders, e.g. firefighters, police, soldiers, where the weareris subject to extreme conditions and performance matters significantly.For example, if heat flux is too low for too long a period of time butskin temperature continues to rise, the wearer is likely to experiencesevere heat distress. Additionally, the ability to detect the wearer'shydration level and the impact of the deterioration of that level isquite useful, and may be derived utilizing the multiple sensors andparameters detected by the system. When a person becomes dehydrated,they typically experience an initially high level of perspiration, whichthen drops off. The body loses its ability to cool, and heat fluxchanges are detected. Additionally, the body temperature rises. At thispoint the cardiovascular system becomes less efficient at transportingoxygen and heart rate increases to compensate, possibly as much as10-20%, necessitating an increase in respiration. At later stages, theuser experiences peripheral vascular shutdown which reduces bloodpressure and results in degradation in activity, awareness andperformance. The monitoring system, which would be capable of measuringand tracking the hydration level, works in conjunction with the ECGdetection, which, by measuring the relative changes in amplitude overtime, in conjunction with expended energy, will recognize and confirmthat amplitude changes are unexpected, or expected because of the eventsto current time.

It will be appreciated that algorithms can use both calibrated sensorvalues and complex derived algorithms. This is effective in predictingendpoints to or thresholds of certain physiological conditions andinforming the wearer or other observer of an approximate measure of timeor other activity until the endpoint is likely to be reached.

Another application of the current invention is as a component in anapparatus for doing wearer fingerprinting and authentication. A 128-Hzheart-rate signal is a rich signal, and personal characteristics such asresting heart rate, beat to beat variability, response to stimuli, andfitness will show up in the signal. These identifying personalcharacteristics can be used to verify that the wearer is indeed theapproved wearer for the device or to identify which of a range ofpossible approved wearers is currently wearing the device. In oneembodiment of this aspect of the invention, only the 128-hz signal andderived parameters from that signal are utilized for identification. Inanother, all of the sensors in the monitor are used together as inputsfor the identification algorithm.

In another application of this aspect of the invention, anauthentication armband can be utilized in a military or first respondersystem as a component in a friend or foe recognition system.

Interaction with other devices is also contemplated. The system canaugment the senses and also the intelligence of other products andcomputer systems. This allows the associated devices to collectivelyknow more about their user and be able to react appropriately, such asautomatically turning the thermostat in the house up or down when asleepor turning the lights on when awakened. In the entertainment context,the detection of certain stress and heart related parameters may beutilized to affect sound, light and other effects in a game, movie orother type of interactive entertainment. Additionally, the user'scondition may be utilized to alter musical programming, such as toincrease the tempo of the music coincident with the changing heart rateof the user during exercise or meditation. Further examples includeturning the car radio down when the person gets stressed while theydrive because they're looking for an address; causing an appliance toprepare a caffeinated drink when the person is tired; matching up peoplein a social environment in the same mood or with the same tastes;utilizing alertness and stress indicators to tune teaching systems suchas intelligent tutors or flight simulators, to maximize the student'sprogress; removing a person's privileges or giving a person privilegesbased on their body state, for example not letting a trucker start uphis truck again until he has had 8 hours of sleep; providing automaticlogin to systems such as the wearer's personal computer based onbiometric fingerprinting; and creating new user interfaces guided inpart or in whole by gross body states for impaired individuals such asautistic children.

Moreover, new human-computer interactions can be envisioned that usebio-states to adjust how the computer reacts to the person. For example,a person is tele-operating a robotic arm. The system can see he is tiredand so smoothes out some of his motion to adjust for some expectedjerkiness due to his fatigue.

Individuals with suspected heart rhythm irregularities will oftenundergo some type of home or ambulatory ECG monitoring. Quite often, theindividual's symptoms appear infrequently and irregularly, such as one aday, once a week, once a month, or even less often. In such cases, it isunlikely that the symptoms will be detected during a visit to the doctorin which classic ECG measurements are taken. Thus the need for home orambulatory ECG monitoring to attempt to capture such infrequentepisodes. The most common home or ambulatory ECG monitoring methods areHolter monitoring, event recording, and continuous loop recording, asdescribed above.

According to another aspect of the present invention, a device asdescribed herein that measures an ECG signal may be adapted andconfigured to perform the functionality of a Holter monitor, an eventrecorder, or a continuous loop recorder. Preferably, such a device maybe armband body monitoring device 300 as illustrated and describedherein. Such a device may be comfortably worn for extended periods oftime, unlike a Holter monitor or an event recorder on a convenientlocation on a limb, such as the upper arm in the case of armband bodymonitoring device 300. In addition, the recorded ECG signals may becombined with other data that is contemporaneously measured by such adevice in accordance with other aspects of the present inventiondescribed herein, including the various physiological parameters and/orcontexts that may be predicted and measured using the algorithmsdescribed herein, to provide automatically context and/or parameterannotated heart related information. For example, as shown in FIG. 12A,a measured ECG signal 70 for a period of time may be mapped or presentedalong with measured parameters such as energy expenditure 75 or even rawsensor values and detected contexts 80 such as walking, driving andresting for the same period of time. This annotated view of the ECGsignal would be useful to a health care provider because it willidentify what the individual was doing while certain heart symptoms wereoccurring and will provide certain other physiological parameters thatmay assist with diagnosis and treatment. This may be accomplished, forexample, by downloading the measured ECG signal, the measured parameteror parameters and the detected contexts to a computing device such as aPC which in turn creates an appropriate display.

It is also well known that there is a circadian pattern to certainarrhythmias or conditions which lead to heart related stress. Suddencardiac arrest, for example, has a high incidence in early morning. Itis therefore anticipated that the detection might be enhanced duringcertain time periods, or that other devices could be cued by themonitoring system to avoid certain coincident or inappropriateactivities or interactions. A pacemaker, for example could raise paceaccording to a preset protocol as the wearer comes out of sleep orwaking the user calmly at the end of a REM stage of sleep.

The system is further applicable in diagnostic settings, such as thecalibration of drug therapies, post-surgical or rehabilitativeenvironments or drug delivery monitoring, with immediate and real timeeffects of these medical applications and procedures being monitoredcontinuously and non-invasively.

This type of application may also be utilized in a mass emergency orother crisis situation, with victims being collected in one location(for example a gymnasium) and are being seen by nurses, EMTs,physicians, volunteers—where this staff is basically short staffed forthis type of situation and diagnosing or keeping watchful monitoringover all the victims now patients (some quite injured and others underobservation in case the injury or shock are delayed in terms ofphysical/tactile/visual symptoms). A system having diagnostic heartrelated capabilities and, optionally, hydration, hypothermia, stress orshock could be distributed upon each victim's entrance for monitoring.The design of the system, which alleviates the need to remove mostclothing for monitoring, would both speed and ease the ability of thecaregivers to apply the devices. This system could send the alerts to acentral system in the facility where the serial number is highlighted,and the attendant is alerted that a condition has been triggered, thenature of the condition as well as the priority. Within thiscollaborative armband scenario, all the armbands around the conditionsensing/triggering armband could also beep or signal differently tofocus the attention of an attendant to that direction more easily.Additionally, certain techniques, as described below, would allow all ofthe armbands to interactively coordinate and validate their relativelocation continuously with the surrounding armbands, allowing thecentral monitoring station to locate where in the facility the locationof any particular armband is located and where specifically are theindividuals who need the most immediate attention.

More specifically, the device could be designed to be part of a networkof devices solving as a network of devices the exact or relativelocations of each device in the network. In this embodiment each devicewould have one or more mechanisms for determining the relative positionof itself to another device in the network. Examples of how this couldbe done include the sending of RF, IR, or acoustic signals between thedevices and using some technique such as time of flight and/or phaseshifts to determine the distance between the devices. It is a knownproblem that methods such as these are prone to errors under real worldcircumstances and in some cases, such as the phase shift method, givethe receiving device an infinite number of periodic solutions to therelative distance question. It is also typical that such devices,because of power limitations, occasional interference from theenvironment and the like, would lose and then later regain contact withother devices in the networks so that at any one time each device mightonly have communication with a subset of the other devices in thenetwork.

Given this ability to establish at each moment in time a relativedistance between each pair of devices, and the ability of the devices toshare what they know with all other devices in the network, for anetwork for N devices, there are a total of (N*(N−1))/2 distances to bemeasured and it is practical that every device could, by passing on allthey know to all the devices they can communicate with at that moment intime, arrive at a state where all devices in the network have allavailable relative distances that could be measured, which would be somesubset of the (N*(N−1))/2 possible distances to be measured, and couldhave updates to this list of numbers quite often, e.g. several times perminute, relative to the speed at which the wearers are changing relativeto each other.

Once each device has a list of these distances, each device effectivelyhas a system of equations and unknowns. For example: A is approximatelyX meters from B, B is approximately Y meters from C, C is approximatelyZ meters from A, A is U meters from D, B is T meters from D, C is Vmeters from D. Alternatively, under the phase shift only model, theseequations could be as follows: A is some integer multiple of six inchesfrom B, B is some integer multiple of eight inches from C, C is someinteger multiple of one foot from D, and D is some integer multiple ofseven inches from A. To the extent there is redundant information in thenetwork, as in the examples just given, and with the possible additionalassumptions about the topology on which the wearers are situated, suchas a flat area, a hill that rises/falls no faster than a grade of 6% orthe like, each device can solve this system of equations and unknowns orequations and inaccurate values to significantly refine the estimates ofthe distance between each pair of devices. These results can be thenshared between devices so that all devices have the most accurate,up-to-date information and all agree, at each moment in time, what theirrelative positions are. This solving of equations can be done through aprocess such as dynamic programming or a matrix solution form such assingular value decomposition. The previous values each wearer's devicehas for its distance to all the other devices can be included in thesecalculations as follows to take advantage for things such as if A wasten feet from B five seconds ago, it is highly unlikely that A is nowtwo hundred feet from B even if that is one of the possible solutions tothe system of equations and unknowns.

An alternative embodiment involves utilizing probabilistic reasoning tokeep track of a probabilistic estimate of the relative location of eachwearer and for taking into account possible sensor noise and expectedmotion. Kalman filters are an example of this sort of reasoning oftenapplied in tracking a single moving entity; extensions to multipleinteracting entities are available.

If these devices are also equipped with ability to know or be told, fromtime to time, their actual or approximate global location, such asthrough an embedded GPS chip, then this information could also be sharedwith all the other devices in the network so that, adjusting for theirrelative distances, each device will then know its global location.

To aid in this process, it is preferred that there be provided at leastone interval where the relative positions are known for the entirenetwork. This, along with frequent updates, relative to the rate theymove relative to each other, to the relative distances of the devices,reduces the possibly solutions for these systems of equations andthereby improves the accuracy of the process. This synchronization ofthe devices could be accomplished for example, for having them togetherin the identical location for a moment before each devices sets out onits own for a time.

Referring to FIG. 13, specific embodiment of a sensor device is shownwhich is in the form of an armband adapted to be worn by an individualon his or her upper arm, between the shoulder and the elbow, which, forconvenience, be referred to as armband body monitoring device 300.Armband sensor device 300 includes housing 305, flexible wing body 307,and, elastic strap 309. Housing 305 and flexible wing body 307 arepreferably made of a flexible urethane material or an elastomericmaterial such as rubber or a rubber-silicone blend by a molding process.Flexible wing body 307 includes first and second wings 311 each having athru-hole 312 located near the ends thereof. First and second wings 311are adapted to wrap around a portion of the wearer's upper arm.

Elastic strap 309 is used to removably affix armband body monitoringdevice 300 to the individual's upper arm. The surface of elastic strapis provided with velcro loops along a portion thereof. Each end ofelastic strap 309 is provided with a velcro hook patch on the bottomsurface and a pull tab. A portion of each pull tab extends beyond theedge of each end 427.

An activation button 314 is provided for appropriate user input, whileLED output indicators 316 provide context-sensitive output. Inparticular, circuit 200 is provided inside housing 305 of armband bodymonitoring device 300, and the various electrodes and sensors identifiedherein are electrically connected thereto, as will be apparent to oneskilled in the art. CPU 165 of circuit 200 would, in this embodiment,preferably be the processing unit forming part of the armband bodymonitoring device circuitry described in U.S. Pat. No. 6,605,038 andU.S. application Ser. No. 10/682,293, the specifications of both whichare hereby incorporated by reference.

Referring now to FIGS. 14 and 15, armband body monitoring device 300 isprovided with additional physiological and/or contextual sensors forsensing various physiological and/or contextual parameters of thewearer, including, but not limited to, GSR sensors 315 for measuring theresistance of the skin to electrical current, a heat flux sensor inthermal communication with heat flux skin interface component 320 formeasuring heat flow off of the body, a skin temperature sensor inthermal communication with skin temperature skin interface component 325for measuring skin temperature, a body motion sensor such as anaccelerometer (not shown) for measuring data relating to body movement,and an ambient temperature sensor (not shown) for measuring thenear-body temperature of the wearer. Referring to FIG. 14, at least one,and preferably two electrode support connectors 318 are provided for thetemporary and removable attachment of any one of a series of electrodesupport modules. Referring to FIG. 15, circuit 200 including electrodes105A through 105D may be provided as part of an armband body monitoringdevice 300 such as are described in the aforementioned U.S. Pat. No.6,605,038 and U.S. application Ser. No. 10/682,293, owned by theassignee of the present invention (see, e.g., sensor devices 400, 800and 1201 described in the '038 patent and/or the '293 application),connected to housing 305 and circuit 200 through insulated wires 310.Electrodes 105′ are illustrated in FIGS. 14, 15 and 18 at alternativelocations at various locations on the housing or support members. It isto be specifically noted that electrodes may be placed at anyappropriate location on or associated with the housing for the purposeof engaging the corresponding appropriate locations on the body fordetecting a signal of appropriate strength and aspect. With respect toFIG. 14, the alternative electrodes 105′ are located within GSR sensors315. With respect to FIG. 15, alternative electrodes 105′ are mounteddirectly within housing 305.

Armband body monitoring device 300 is designed to be worn on the back ofthe upper arm, in particular on the triceps muscle of the upper arm,most preferably the left arm. Referring to the specific embodiment shownin FIG. 15, when worn on the upper left arm, electrode 105A is incontact with the deltoid muscle, electrode 105B is in contact with thetriceps muscle, electrode 105C and electrode 105D are in contact with anarea of the muscle which may not produce a detectable heart relatedsignal but permits the detection of baseline EMG noise. Preferably,first and second imaginary diagonal lines connect electrode 105A toelectrode 105B and electrode 105C to electrode 105D, respectively, atangles of approximately 31 degrees from vertical. In this embodiment,electrodes 105A and 105B may be paired with one another to detect afirst signal and electrodes 105C and 105D may be paired with one anotherto detect a second signal as described above, which signals are summedtogether by summation circuit 170 of circuit 200.

Referring now to FIG. 16, an alternative embodiment of the deviceillustrated in FIG. 15 is shown. Electrode support connector 318 isprovided for the purpose of physically supporting a sensor or sensorsupport housing as well as establishing electrical communicationtherewith. Electrode support connector 318 may be a plug-in or snap-inconnector of the pin type which will provide good physical support whileallowing some degree of movement or rotation of the sensor or sensorhousing while mounted on the body. Preferably, the device and sensor orsensor support, as appropriate, are integrated for best physical andelectrical connection. A multichannel electrical connection is alsoprovided according to conventional means, typically utilizing multipleindependently insulated segments of the supporting connector. A sensorsupport housing 322 may be provided for the support and positioning ofelectrode 105, as shown in FIG. 16, or the electrode 105 or other sensormay be directly and independently mounted to electrode support connector318. In this embodiment, the support housing 322, is entirelysubstituted by the electrode 105 itself in an identical physicalarrangement. The electrode 105 may be positioned at any point on thesurface of support housing 322, and need not be located at the center,as shown in FIG. 16. Additionally, sensors need not be a point source ofinformation, as they are conventionally applied and utilized. The sensormay further be comprised of a broad segment of sensitive material whichcovers a substantial portion of the housing surface in order to maximizethe location of the appropriate point for signal detection within thesurface area of the sensor. In the event that a support housing 322 isutilized, a flexible material is utilized to permit the housing toconform to the surface of the arm upon which it is mounted to ensuregood contact with the skin and underlying tissue. This is equallyapplicable to the embodiment shown in FIG. 15. It is also to bespecifically noted that each of the sensor, electrode and supporthousing embodiments described and illustrated herein areinterchangeable, with certain shapes or other physical parameters beingselected for particular applications. Additionally, it is to beunderstood that the number and arrangement of the sensors, electrodesand support housings are not limited by the embodiments shown in theFigures, but may be interchanged as well. Lastly, in order to establisha particular geometry of sensors, electrodes or an array of the same,the housing 305 of the device may be modified to be elongated ordiminished in any particular dimension for the purpose of improving thesignal, as described above.

With reference to FIG. 17, an additional alternative embodiment isillustrated which provides a similar orientation of electrodes as thatillustrated in FIG. 16, with the support housing 322 having a moreelongated geometry. Typically, more elongated or outboard electrodeplacements will necessitate the use of more firm materials for thesupport housing 322, in order to maintain good skin contact. It is to bespecifically noted that any of the housing embodiments shown andillustrated may further comprise a flexible or partly flexible housingsection which is pre-molded in a curved embodiment in order to exertpressure against the skin.

FIG. 18 illustrates an asymmetrical arrangement of the support housing322 having a lateral support arm 323 which is intended to specificallyplace the upper and lower electrodes 105 adjacent to the deltoid andbrachialis sections of the tricep muscle, respectively, of the humanupper arm. Lateral support arm 323 may also be separated from supporthousing 322 along the chain line sections indicated in the figure andaffixed to wings 311 by restraints 324. Housing 305 or wings 311 mayfurther be extended beyond the generally ovoid shape illustrated in thefigures hereto into any particular shape necessary to engage theappropriate locations on the body. More particularly, irregularextensions of housing 305 or wings 311 are contemplated to mountalternative electrodes 105′.

FIG. 19 illustrates support housing 322 having a particular ovoid shape.

FIG. 20 illustrates an alternative embodiment similar to thatillustrated in FIG. 15, however only one outboard or external electrode105 is utilized, which is provided with electrical communication throughinsulated wire 310. Any of the previously identified electrodegeometries may be utilized for affixation to the second electrodesupport connector 318. The use of the outboard electrode 105 connectedto insulated wire 310, sometimes identified as a fly lead, is adaptedfor particular location on a remote section of the body which rendersthe creation of an integrated housing 305 of armband body monitoringdevice 300 impractical. FIG. 21 illustrates the embodiment of FIG. 20mounted upon a human upper arm A. Armband body monitoring device 300 isplaced adjacent the skin at an appropriate position and the elasticstrap 309 encircles the arm and is pulled tight enough to firmly securethe housing without reducing blood flow. Sensor support housing 322supports electrode 105 (not shown) and is held in place by adhesivesupport 323 which mounts support housing 322 to the skin. It is to bespecifically noted that the location of the support housing is notlimited to the location illustrated in FIG. 21, but may extend to anypart of the body, including the other arm of the wearer. The mostpreferred embodiment seeks to minimize the use and length of insulatedwires 310.

FIG. 22 illustrates an alternative embodiment which presents a moremodular approach to the interface between the electrodes 105, supporthousing 322 and housing 305. Housing 305 is provided with a similar skinengagement face (not shown) as illustrated in FIG. 14. An integratedremovable support housing 322, which may be disposable, comprises boththe support material for exerting the appropriate force upon theelectrodes (not shown) on the underside of the support housing 322against the skin, the electrodes themselves, as well as the electronicconnections between the electrodes and the housing 305. Support housingis provided with at least one electrode contact 324 for electronicengagement with the housing, and may be suited for engagement witheither electrode support connectors 318 or GSR sensors 315 which havebeen specifically adapted to communicate with electrodes 105 inconjunction with support housing 324. An optional adhesive support 323may also be provided on the underside of support housing 322. In analternative embodiment, adhesive support 323 may provide the sole meansfor retention of housing 305 on the user's arm. Support housing 322 mayalso be supported on the skin solely by the force of the housing 305 asrestrained on the arm by elastic strap 309, or in conjunction with otherhousing or garment support devices as described in U.S. patentapplication Ser. No. 10/227,575, the specification of which isincorporated by reference herein. An output screen 327 is illustratedherein on the upper surface of housing 305 for displaying certainperformance or other status information to the user. It is to bespecifically noted that the output screen may be of any type, includingbut not limited to an electrochemical or LCD screen, may be disposable,and may further be provided on any of the embodiments illustratedherein.

FIGS. 23A-C illustrate yet another embodiment of the device whichincorporates a slimmer housing 305, which is provided with aperture 329for functionality which is not relevant hereto. An adhesive support 323is mounted semi-equatorially and may contain electrodes 105, which mayalso be mounted on the underside of housing 305. In operation, thehousing is affixed to the body through the use of the adhesive providedon adhesive support 323, which maintains a consistent contact betweenhousing 305 and/or electrodes 105 and/or any other relevant sensorscontained within housing 305 and the body. It is to be specificallynoted that this adhesive embodiment may be mounted at any point on thehuman body and is not limited to any particular appendage or location.

An additional aspect of the embodiments illustrated herein is theopportunity to select certain aspects of each device and place the samein disposable segments of the device, as illustrated with particularityin FIG. 22. This may be utilized in conjunction with a permanent, ordurable housing 305 which contains the remaining aspects of the device'sfunctionality. Additionally, the entire device could be rendered in adisposable format, which anticipates a limited continuous wearing timefor each system. In this embodiment, as mentioned previously, the entiredevice might be rendered in a patch-like flexible housing, polymer,film, textile or other support envelope, all of which could bespring-like and which may be mounted anywhere on the body. This includesa textile material which has the electrodes and other electronicsinterwoven within the material itself, and which exerts sufficient forceagainst the body to maintain appropriate contact for the reception ofthe signals. Fabrics such as Aracon, a metal clad textile with thestrength characteristics of Kevlar, both manufactured by DuPont, arecapable of carrying an electrical current or signal therethrough.ElekTex from Eleksen Ltd is a soft textile appropriate for use inclothing or bedding which contains electrodes and/or sensors which candetect movement or pressure. These fabrics could be utilized incombination with the device components in a wearable shirt or othergarment which could both sense the appropriate signals as well asprovide a network for the interconnection of the various electricalcomponents which could be located at various convenient places withinthe garment.

The ECG wave form collected from inside any of the equivalence classregions will not necessarily have the shape of a standard ECG wave form.When this is the case, a mapping can be created between a ECG wave formtaken within a single equivalence class region and ECG wave forms takenbetween equivalence class regions. This can be done using the algorithmdevelopment process described above, creating a function that warps thewithin equivalence class region to be clearer when displayed as astandard ECG wave form.

Although particular embodiments of the present invention have beenillustrated in the accompanying drawings and described in the foregoingdetailed description, it is to be further understood that the presentinvention is not to be limited to just the embodiments disclosed, butthat they are capable of numerous rearrangements, modifications andsubstitutions, as identified in the following claims.

1. (canceled)
 2. An apparatus to determine physiological status of anindividual, comprising: a wearable sensor device adapted to be worn inan equivalence region of the individual; at least two electrodesattached to said wearable sensor device, said electrodes adapted to bemounted within said equivalence region and to detect a heart-relatedsignal; and a processor in electronic communication with said at leasttwo electrodes and programmed to determine a sleep-related parameter ofthe individual.
 3. The apparatus of claim 2, wherein the sleep-relatedparameter is a sleep stage.
 4. The apparatus of claim 3, wherein saidindividual's sleep stage is REM.
 5. The apparatus of claim 2, furthercomprising at least one sensor mounted to said wearable sensor device,said at least one sensor selected from the group consisting of a heatflux sensor, a galvanic skin response sensor, a skin temperature sensor,and an accelerometer.
 6. The apparatus of claim 2, wherein the processoris programmed to utilize said at least one sensor and said at least twoelectrodes to determine additional physiological parameters of theindividual.
 7. The apparatus of claim 2, further comprising an outputdevice.
 8. The apparatus of claim 7, wherein said processor is furtherprogrammed to cause the output device to output an alarm based on thesleep stage of the individual.
 9. The apparatus of claim 7, wherein saidwherein said processor is further programmed to cause the output deviceto output data relating to the sleep stage of the individual.